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Wireless-Innovation-Forum / 6-GHz-AFC

This repository contains code and data for testing the compliance of Automated Frequency Coordinator (AFC) software. The AFC is defined by the FCC in proceeding 18-295 on Unlicensed Use of the 6 GHz Band. This repository contains procedures, documentation, and tests for such software, and for the devices authorized by it. To contribute, please first read the CONTRIBUTING file in the repository for instructions.
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AFCS.SIP2 Not able to match few expected incumbents #50

Closed alexcpn closed 8 months ago

alexcpn commented 10 months ago

While running the calculation for AFCS.SIP2

https://github.com/Wireless-Innovation-Forum/6-GHz-AFC/blob/main/src/harness/inquiries/AFCS.SIP.2.json https://github.com/Wireless-Innovation-Forum/6-GHz-AFC/blob/main/src/harness/masks/AFCS.SIP.2_mask.json

I am missing few Incumbents. I have been trying to debug this for more than two weeks, increasing search radius to even 250 Km but to no avail.

The one from 5930-5949 5930-5949,upperBound:11.1 nominalValue:9.1 is causing the most deviation.

The incumbent I got at this is uuid= 3225580 callsign =WQMK438 (5940.2, 5950.2). But the PSD values I got for this are very high, how much ever I recheck or calculate.

Is it possible to tell if I am missing any incumbent at this range, or is this the right one, but there is some calculation or code error in this

[INFO] ********************************************************************************
[INFO] Going to process UUID = 3225580 call_sign = 'WQMK438
[INFO] accesspoint_loc =(38.377266, -78.468021) receiver_loc=(38.80405555555555, -78.68863888888889)
[INFO] Processing UUID = 3225580 call_sign = 'WQMK438' R to AP Dist =51127.416904742524 m, CF =5945.2 Mhz r_antenna_make= RFS r_antenna_model=PAD8-59A
[INFO] uuid: 3225580
[INFO] call_sign: WQMK438
[INFO] frequency_assigned: 5945.2
[INFO] receiver_loc: (38.80405555555555, -78.68863888888889)
[INFO] t_loc_num: 1
[INFO] t_path_num: 1
[INFO] t_ant_num: 1
[INFO] receiver_to_ap_dist_m: 51127.416904742524
[INFO] r_height_to_center_raat: 29.0
[INFO] r_gain: 41.3
[INFO] r_diversity_gain: None
[INFO] r_azimuth: 0.0
[INFO] r_antenna_make: RFS
[INFO] r_antenna_model: PAD8-59A
[INFO] r_loc_num: 2
[INFO] r_path_num: 1
[INFO] r_ant_num: 1
[INFO] polarization_code: None
[INFO] line_loss: None
[INFO] r_diameter_ft: 8.0
[INFO] r_diameter_m: 2.44
[INFO] ant_category: B1
[INFO] transmitter_loc: (39.00811111111111, -78.32655555555556)
[INFO] t_height_to_center_raat: 40.8
[INFO] t_gain: 41.3
[INFO] t_diversity_gain: None
[INFO] t_azimuth: 234.3
[INFO] t_antenna_make: RFS
[INFO] t_antenna_model: PAD8-59A
[INFO] bandwidth: 10.0
[INFO] frequency_range: (5940.2, 5950.2)
[INFO] accesspoint_loc: (38.377266, -78.468021)
[INFO] accesspoint_ht_m: 300.0
[INFO] accesspoint_vtunc_m: 30
[INFO] Free Space Path Loss =142.0981785994584
[INFO] accesspoint_elev=528.5480346679688 receiver_elev=515.4696044921875 transmitter_elev=189.72903442382812
[INFO] GRx = 41.3 dB
[INFO] centerFreq of Receiver =5945.2 Mhz
[INFO] ant_category = B1 
[INFO] Angle 1: Azimuthal discrimination angle:R-AP The azimuthal discrimination angle,  θAZ_Disc= 103.95632959999497 degress
[INFO] Angle 2: Elevation angle: R-T The receive antenna boresight elevation angle (from transmmitter) θEL_Disc= -0.5949928799295378 degrees
[INFO] Angle 3:elevation angle: R-AP The elevation angle from the  receive antenna to the evaluation phi= 0.14603450937594242 degrees
[INFO] --------------------------------------------------------------------------------
[INFO] Overall discrimination angle, 𝛾 =103.9570781997175 degress
[INFO] --------------------------------------------------------------------------------
[INFO] GRx=41.3 >= 38:HP Calcualted:rpe=-6.844350407395254 rpe2-GRx=-48.14435040739525
[INFO] 2.b GRx=41.3 >= 38:B1:rpe=-39 from Radiation Supression Table
[INFO] GRx=41.3 >= 38:HP Calcualted:rpe=-6.844350407395254 rpe2-GRx=-48.14435040739525
[INFO] 2.b GRx=41.3 >= 38:B1:rpe=-39 from Radiation Supression Table
[INFO] --------------------------------------------------------------------------------
[INFO] The azimuthal discrimination= GAZ-Disc =-39 This should be negative
[INFO] The elevation discrimination  GEL-Disc in dB = -39 contribution This should be Negative!)
[INFO] --------------------------------------------------------------------------------
[INFO] GRx_Effectivein dB = -0.7000000000000086 [41.3 -38.99999991940093] [-8.059907820166229e-08]
[INFO] ----GRx_Effective in dB = -0.7000000000000086
[INFO] Near field adjustments do not have to be done
[INFO] Elevation Profile and ITM losss caluclated  in 0.000389 seconds
[INFO] ITM loss is 258.0874100223423
[INFO] Path Loss used =258.0874100223423
[INFO] building_entry_loss = 0 ap_indoor=False
[INFO] interference = -258.78741002234227 
[INFO] transmit_power PSD = 142.78741002234227 dBm/MHz

Expected vs Calculated

[INFO] Expected Frequency
[INFO] 5925-5930,upperBound:23.0 nominalValue:23.0
[INFO] 5930-5949,upperBound:11.1 nominalValue:9.1
[INFO] 5949-6050,upperBound:11.2 nominalValue:9.2
[INFO] 6050-6078,upperBound:23.0 nominalValue:23.0
[INFO] 6078-6109,upperBound:-0.4 nominalValue:-2.4
[INFO] 6109-6137,upperBound:23.0 nominalValue:23.0
[INFO] 6137-6168,upperBound:-0.3 nominalValue:-2.3
[INFO] 6168-6211,upperBound:23.0 nominalValue:23.0
[INFO] 6211-6241,upperBound:14.3 nominalValue:12.3
[INFO] 6241-6302,upperBound:7.1 nominalValue:5.1
[INFO] 6302-6331,upperBound:17.2 nominalValue:15.2
[INFO] 6331-6359,upperBound:20.9 nominalValue:18.9
[INFO] 6359-6392,upperBound:18.2 nominalValue:16.2
[INFO] 6392-6420,upperBound:18.3 nominalValue:16.3
[INFO] 6420-6425,upperBound:23.0 nominalValue:23.0
[INFO] 6525-6540,upperBound:23.0 nominalValue:23.0
[INFO] 6540-6570,upperBound:23.0 nominalValue:22.9
[INFO] 6570-6580,upperBound:23.0 nominalValue:23.0
[INFO] 6580-6590,upperBound:16.6 nominalValue:14.6
[INFO] 6590-6600,upperBound:23.0 nominalValue:22.7
[INFO] 6600-6610,upperBound:23.0 nominalValue:23.0
[INFO] 6610-6640,upperBound:17.1 nominalValue:15.1
[INFO] 6640-6650,upperBound:23.0 nominalValue:23.0
[INFO] 6676-6680,upperBound:19.9 nominalValue:17.9
[INFO] 6680-6694,upperBound:23.0 nominalValue:21.3
[INFO] 6694-6700,upperBound:23.0 nominalValue:21.4
[INFO] 6700-6710,upperBound:15.4 nominalValue:13.4
[INFO] 6710-6770,upperBound:23.0 nominalValue:23.0
[INFO] 6770-6800,upperBound:20.0 nominalValue:18.0
[INFO] 6800-6803,upperBound:23.0 nominalValue:23.0
[INFO] 6803-6808,upperBound:12.8 nominalValue:10.8
[INFO] 6808-6810,upperBound:23.0 nominalValue:23.0
[INFO] 6810-6820,upperBound:23.0 nominalValue:21.0
[INFO] 6820-6850,upperBound:23.0 nominalValue:23.0
[INFO] 6850-6860,upperBound:8.8 nominalValue:6.8
[INFO] 6860-6870,upperBound:17.1 nominalValue:15.1
[INFO] 6870-6875,upperBound:23.0 nominalValue:23.0
[INFO] Calculated Frequency
[INFO] 5925.0-5930.025  psd: 23.0
[INFO] 5930.025-5990.025  psd: 15.784689601165127
[INFO] 5990.025-6049.325  psd: 10.703737533416259
[INFO] 6049.325-6108.625  psd: 17.911661857844592
[INFO] 6108.625-6137.75  psd: 23.0
[INFO] 6137.75-6167.75  psd: -5.178939698339647
[INFO] 6167.75-6211.89  psd: 23.0
[INFO] 6211.89-6241.89  psd: 9.685669535605086
[INFO] 6241.89-6301.365  psd: 12.380359164661826
[INFO] 6301.365-6330.84  psd: 11.794109109088566
[INFO] 6330.84-6359.965  psd: 23.0
[INFO] 6359.965-6419.965  psd: 21.65690619256381
[INFO] 6419.965-6425.0  psd: 23.0
[INFO] 6525.0-6540.0  psd: 23.0
[INFO] 6540.0-6570.0  psd: 15.737189287409763
[INFO] 6570.0-6590.0  psd: 23.0
[INFO] 6590.0-6600.0  psd: 19.745014053790953
[INFO] 6600.0-6610.0  psd: 21.94745000622069
[INFO] 6610.0-6640.0  psd: 11.105362298937578
[INFO] 6640.0-6640.0  psd: 12.897827283578692
[INFO] 6640.0-6670.0  psd: 21.407024789643486
[INFO] 6670.0-6675.2  psd: 0
[INFO] 6675.2-6700.0  psd: 23.0
[INFO] 6700.0-6710.0  psd: 10.891977330796422
[INFO] 6710.0-6740.0  psd: 20.96310494836436
[INFO] 6740.0-6770.0  psd: 21.021096888872478
[INFO] 6770.0-6800.0  psd: 8.081817084848169
[INFO] 6800.0-6800.0  psd: 17.75008345597371
[INFO] 6800.0-6803.75  psd: 23.0
[INFO] 6803.75-6807.5  psd: 8.015087793907895
[INFO] 6807.5-6817.5  psd: 23.0
[INFO] 6817.5-6822.5  psd: 22.722624097951012
[INFO] 6822.5-6860.0  psd: 23.0
[INFO] 6860.0-6870.0  psd: 12.10214734191419
[INFO] 6870.0-6875.0  psd: 23.0
[WARNING] gc=131 cn=1,freq_range=(5945.0, 5965.0),expected_eirp={'nominalValue': 22.2, 'upperBound': 24.2} calc_eirp=5.98 diff=18.22
[WARNING] gc=131 cn=5,freq_range=(5965.0, 5985.0),expected_eirp={'nominalValue': 22.2, 'upperBound': 24.2} calc_eirp=5.98 diff=18.22
[WARNING] gc=131 cn=9,freq_range=(5985.0, 6005.0),expected_eirp={'nominalValue': 22.2, 'upperBound': 24.2} calc_eirp=5.98 diff=18.22
[WARNING] gc=131 cn=25,freq_range=(6065.0, 6085.0),expected_eirp={'nominalValue': 10.6, 'upperBound': 12.6} calc_eirp=7.73 diff=4.87
[WARNING] gc=131 cn=29,freq_range=(6085.0, 6105.0),expected_eirp={'nominalValue': 10.6, 'upperBound': 12.6} calc_eirp=7.73 diff=4.87
[WARNING] gc=131 cn=33,freq_range=(6105.0, 6125.0),expected_eirp={'nominalValue': 10.6, 'upperBound': 12.6} calc_eirp=7.73 diff=4.87
[WARNING] gc=131 cn=37,freq_range=(6125.0, 6145.0),expected_eirp={'nominalValue': 10.7, 'upperBound': 12.7} calc_eirp=7.83 diff=4.87
[WARNING] gc=131 cn=41,freq_range=(6145.0, 6165.0),expected_eirp={'nominalValue': 10.7, 'upperBound': 12.7} calc_eirp=7.83 diff=4.87
[WARNING] gc=131 cn=45,freq_range=(6165.0, 6185.0),expected_eirp={'nominalValue': 10.7, 'upperBound': 12.7} calc_eirp=7.83 diff=4.87
[WARNING] gc=131 cn=53,freq_range=(6205.0, 6225.0),expected_eirp={'nominalValue': 25.3, 'upperBound': 27.3} calc_eirp=22.7 diff=4.60
[ERROR] gc=131 cn=57,freq_range=(6225.0, 6245.0) expected_eirp={'nominalValue': 18.1, 'upperBound': 20.1} calc_eirp=22.7 diff=2.60
[ERROR] gc=131 cn=61,freq_range=(6245.0, 6265.0) expected_eirp={'nominalValue': 18.1, 'upperBound': 20.1} calc_eirp=25.36 diff=5.26
[ERROR] gc=131 cn=65,freq_range=(6265.0, 6285.0) expected_eirp={'nominalValue': 18.1, 'upperBound': 20.1} calc_eirp=25.36 diff=5.26
[ERROR] gc=131 cn=69,freq_range=(6285.0, 6305.0) expected_eirp={'nominalValue': 18.1, 'upperBound': 20.1} calc_eirp=24.8 diff=4.70
[WARNING] gc=131 cn=73,freq_range=(6305.0, 6325.0),expected_eirp={'nominalValue': 28.2, 'upperBound': 30.2} calc_eirp=24.8 diff=5.40
[WARNING] gc=131 cn=77,freq_range=(6325.0, 6345.0),expected_eirp={'nominalValue': 28.2, 'upperBound': 30.2} calc_eirp=24.8 diff=5.40
[ERROR] gc=131 cn=81,freq_range=(6345.0, 6365.0) expected_eirp={'nominalValue': 29.2, 'upperBound': 31.2} calc_eirp=34.67 diff=3.47
[ERROR] gc=131 cn=89,freq_range=(6385.0, 6405.0) expected_eirp={'nominalValue': 29.3, 'upperBound': 31.3} calc_eirp=34.67 diff=3.37
[ERROR] gc=131 cn=93,freq_range=(6405.0, 6425.0) expected_eirp={'nominalValue': 29.3, 'upperBound': 31.3} calc_eirp=34.67 diff=3.37
[WARNING] gc=131 cn=117,freq_range=(6525.0, 6545.0),expected_eirp={'nominalValue': 35.9, 'upperBound': 36.0} calc_eirp=28.75 diff=7.25
[WARNING] gc=131 cn=121,freq_range=(6545.0, 6565.0),expected_eirp={'nominalValue': 35.9, 'upperBound': 36.0} calc_eirp=28.75 diff=7.25
[ERROR] gc=131 cn=129,freq_range=(6585.0, 6605.0) expected_eirp={'nominalValue': 27.6, 'upperBound': 29.6} calc_eirp=32.76 diff=3.16
[WARNING] gc=131 cn=133,freq_range=(6605.0, 6625.0),expected_eirp={'nominalValue': 28.1, 'upperBound': 30.1} calc_eirp=24.12 diff=5.98
[WARNING] gc=131 cn=137,freq_range=(6625.0, 6645.0),expected_eirp={'nominalValue': 28.1, 'upperBound': 30.1} calc_eirp=24.12 diff=5.98
[WARNING] gc=131 cn=149,freq_range=(6685.0, 6705.0),expected_eirp={'nominalValue': 26.4, 'upperBound': 28.4} calc_eirp=23.9 diff=4.50
[WARNING] gc=131 cn=153,freq_range=(6705.0, 6725.0),expected_eirp={'nominalValue': 26.4, 'upperBound': 28.4} calc_eirp=23.9 diff=4.50
[WARNING] gc=131 cn=165,freq_range=(6765.0, 6785.0),expected_eirp={'nominalValue': 31.0, 'upperBound': 33.0} calc_eirp=21.09 diff=11.91
[WARNING] gc=131 cn=169,freq_range=(6785.0, 6805.0),expected_eirp={'nominalValue': 23.8, 'upperBound': 25.8} calc_eirp=21.03 diff=4.77
[WARNING] gc=131 cn=173,freq_range=(6805.0, 6825.0),expected_eirp={'nominalValue': 23.8, 'upperBound': 25.8} calc_eirp=21.03 diff=4.77
[ERROR] gc=131 cn=181,freq_range=(6845.0, 6865.0) expected_eirp={'nominalValue': 19.8, 'upperBound': 21.8} calc_eirp=25.11 diff=3.31
[WARNING] gc=132 cn=3,freq_range=(5945.0, 5985.0),expected_eirp={'nominalValue': 25.2, 'upperBound': 27.2} calc_eirp=8.99 diff=18.21
[WARNING] gc=132 cn=11,freq_range=(5985.0, 6025.0),expected_eirp={'nominalValue': 25.2, 'upperBound': 27.2} calc_eirp=8.99 diff=18.21
[WARNING] gc=132 cn=27,freq_range=(6065.0, 6105.0),expected_eirp={'nominalValue': 13.6, 'upperBound': 15.6} calc_eirp=10.74 diff=4.86
[WARNING] gc=132 cn=35,freq_range=(6105.0, 6145.0),expected_eirp={'nominalValue': 13.6, 'upperBound': 15.6} calc_eirp=10.74 diff=4.86
[WARNING] gc=132 cn=43,freq_range=(6145.0, 6185.0),expected_eirp={'nominalValue': 13.7, 'upperBound': 15.7} calc_eirp=10.84 diff=4.86
[WARNING] gc=132 cn=51,freq_range=(6185.0, 6225.0),expected_eirp={'nominalValue': 28.3, 'upperBound': 30.3} calc_eirp=25.71 diff=4.59
[ERROR] gc=132 cn=59,freq_range=(6225.0, 6265.0) expected_eirp={'nominalValue': 21.1, 'upperBound': 23.1} calc_eirp=25.71 diff=2.61
[ERROR] gc=132 cn=67,freq_range=(6265.0, 6305.0) expected_eirp={'nominalValue': 21.1, 'upperBound': 23.1} calc_eirp=27.81 diff=4.71
[WARNING] gc=132 cn=75,freq_range=(6305.0, 6345.0),expected_eirp={'nominalValue': 31.2, 'upperBound': 33.2} calc_eirp=27.81 diff=5.39
[ERROR] gc=132 cn=91,freq_range=(6385.0, 6425.0) expected_eirp={'nominalValue': 32.3, 'upperBound': 34.3} calc_eirp=36.0 diff=1.70
[WARNING] gc=132 cn=131,freq_range=(6585.0, 6625.0),expected_eirp={'nominalValue': 30.6, 'upperBound': 32.6} calc_eirp=27.13 diff=5.47
[WARNING] gc=132 cn=155,freq_range=(6705.0, 6745.0),expected_eirp={'nominalValue': 29.4, 'upperBound': 31.4} calc_eirp=26.91 diff=4.49
[WARNING] gc=132 cn=163,freq_range=(6745.0, 6785.0),expected_eirp={'nominalValue': 34.0, 'upperBound': 36.0} calc_eirp=24.1 diff=11.90
[WARNING] gc=132 cn=171,freq_range=(6785.0, 6825.0),expected_eirp={'nominalValue': 26.8, 'upperBound': 28.8} calc_eirp=24.04 diff=4.76
[ERROR] gc=132 cn=179,freq_range=(6825.0, 6865.0) expected_eirp={'nominalValue': 22.8, 'upperBound': 24.8} calc_eirp=28.12 diff=3.32
[WARNING] gc=133 cn=7,freq_range=(5945.0, 6025.0),expected_eirp={'nominalValue': 28.2, 'upperBound': 30.2} calc_eirp=12.0 diff=18.20
[WARNING] gc=133 cn=23,freq_range=(6025.0, 6105.0),expected_eirp={'nominalValue': 16.6, 'upperBound': 18.6} calc_eirp=13.75 diff=4.85
[WARNING] gc=133 cn=39,freq_range=(6105.0, 6185.0),expected_eirp={'nominalValue': 16.6, 'upperBound': 18.6} calc_eirp=13.75 diff=4.85
[ERROR] gc=133 cn=55,freq_range=(6185.0, 6265.0) expected_eirp={'nominalValue': 24.1, 'upperBound': 26.1} calc_eirp=28.72 diff=2.62
[ERROR] gc=133 cn=71,freq_range=(6265.0, 6345.0) expected_eirp={'nominalValue': 24.1, 'upperBound': 26.1} calc_eirp=30.83 diff=4.73
[WARNING] gc=133 cn=167,freq_range=(6745.0, 6825.0),expected_eirp={'nominalValue': 29.8, 'upperBound': 31.8} calc_eirp=27.05 diff=4.75
[WARNING] gc=134 cn=15,freq_range=(5945.0, 6105.0),expected_eirp={'nominalValue': 19.6, 'upperBound': 21.6} calc_eirp=15.01 diff=6.59
[WARNING] gc=134 cn=47,freq_range=(6105.0, 6265.0),expected_eirp={'nominalValue': 19.7, 'upperBound': 21.7} calc_eirp=16.76 diff=4.94
[ERROR] gc=134 cn=79,freq_range=(6265.0, 6425.0) expected_eirp={'nominalValue': 27.2, 'upperBound': 29.2} calc_eirp=33.84 diff=4.64

Visually

https://docs.google.com/spreadsheets/d/1IY_uOQtuQigSxaF9nK479enoBHdUCkOuqDDBK4Ysfow/edit?usp=sharing

image

alexcpn commented 10 months ago

@AEgbert could you help here a bit. Really stuck here at SIP2 and few others like SIP4.

These are the incumbent frequencies I have got

[INFO] -uuid--call_sign----incumbent frequency range-----------
[INFO] 3534062 WQST407 (5930.025, 5990.025)
[INFO] 1051454 WPOU851 (5930.025, 5990.025)
[INFO] 1045356 WPNL763 (5930.025, 5990.025)
[INFO] 1004792 WMQ731 (5930.025, 5990.025)
[INFO] 4312188 WRHY689 (5930.025, 5990.025)
[INFO] 3970980 WRAF682 (5930.025, 5990.025)
[INFO] 3969917 WRAF301 (5930.025, 5990.025)
[INFO] 1045354 WPNL761 (5930.025, 5990.025)
[INFO] 3257882 WQNE951 (5930.025, 5990.025)
[INFO] 2969284 WQIA733 (5930.025, 5990.025)
[INFO] 4119305 WRCU974 (5930.025, 5990.025)
[INFO] 4381249 WRKP819 (5930.025, 5990.025)
[INFO] 3939329 WQZR472 (5930.2, 5960.2)
[INFO] 1304851 WPQX319 (5930.2, 5960.2)
[INFO] 3428356 WQQG597 (5930.2, 5960.2)
[INFO] 1015827 WNEU223 (5930.2, 5960.2)
[INFO] 3037695 WQJF547 (5930.2, 5960.2)
[INFO] 3296569 WQNV904 (5930.2, 5960.2)
[INFO] 1043583 WPNI368 (5930.2, 5960.2)
[INFO] 975620 WEG314 (5930.2, 5960.2)
[INFO] 3935501 WQZP778 (5930.2, 5960.2)
[INFO] 3045911 WQJJ255 (5930.2, 5960.2)
[INFO] 3102937 WQKG731 (5930.2, 5960.2)
[INFO] 2704019 WQCJ948 (5930.2, 5960.2)
[INFO] 3038036 WQJF661 (5930.2, 5960.2)
[INFO] 3662682 WQVE303 (5930.2, 5960.2)
[INFO] 3223613 WQMJ437 (5930.2, 5960.2)
[INFO] 3288163 WQNS678 (5930.2, 5960.2)
[INFO] 1093209 WPQP308 (5930.2, 5960.2)
[INFO] 3687802 WQVN528 (5930.2, 5960.2)
[INFO] 1006205 WMS821 (5930.2, 5960.2)
[INFO] 2543766 WPYK290 (5930.2, 5960.2)
[INFO] 3653853 WQVA547 (5930.2, 5960.2)
[INFO] 1014345 WNER277 (5930.2, 5960.2)
[INFO] 1039555 WPNC314 (5930.2, 5960.2)
[INFO] 3454993 WQQV497 (5930.2, 5960.2)
[INFO] 3789547 WQXI258 (5930.2, 5960.2)
[INFO] 1014359 WNER297 (5930.2, 5960.2)
[INFO] 3304513 WQNZ561 (5930.2, 5960.2)
[INFO] 3536404 WQSU882 (5930.2, 5960.2)
[INFO] 1006417 WMT235 (5930.2, 5960.2)
[INFO] 1014344 WNER276 (5930.2, 5960.2)
[INFO] 1002064 WMJ669 (5930.2, 5960.2)
[INFO] 980226 WHC362 (5930.2, 5960.2)
[INFO] 1013286 WNEP339 (5930.2, 5960.2)
[INFO] 4266672 WRFL994 (5930.2, 5960.2)
[INFO] 4441402 WRML347 (5930.2, 5960.2)
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[INFO] 4343469 WRJR698 (6803.35, 6826.65)
[INFO] 3049695 WQJK741 (6803.75, 6807.5)
[INFO] 3503886 WQRX731 (6803.75, 6807.5)
[INFO] 2351777 WPTF550 (6803.75, 6807.5)
[INFO] 1014360 WNER298 (6807.5, 6812.5)
[INFO] 2917938 WQHA308 (6807.5, 6810.0)
[INFO] 4430836 WRMF700 (6808.875, 6812.375)
[INFO] 3304771 WQNZ645 (6810.0, 6820.0)
[INFO] 1012483 WNEN343 (6810.0, 6820.0)
[INFO] 1049782 WPOS639 (6810.0, 6820.0)
[INFO] 959027 KFX74 (6810.0, 6820.0)
[INFO] 2693739 WQCE459 (6810.0, 6820.0)
[INFO] 2932011 WQHG837 (6810.0, 6820.0)
[INFO] 971826 WBX365 (6810.0, 6820.0)
[INFO] 2415432 WPUZ428 (6810.0, 6820.0)
[INFO] 1098736 WPQR465 (6810.0, 6820.0)
[INFO] 2523759 WPXV623 (6810.0, 6820.0)
[INFO] 975620 WEG314 (6810.0, 6820.0)
[INFO] 4343460 WRJR690 (6810.0, 6820.0)
[INFO] 1098735 WPQR464 (6810.0, 6820.0)
[INFO] 969965 WAZ571 (6810.0, 6820.0)
[INFO] 967725 KZK54 (6812.5, 6817.5)
[INFO] 1080088 WPON740 (6815.0, 6816.25)
[INFO] 1933928 WPQZ379 (6817.5, 6822.5)
[INFO] 1029670 WNTU678 (6817.5, 6822.5)
[INFO] 2917958 WQHA312 (6817.5, 6820.0)
[INFO] 2361115 WPTM669 (6820.0, 6830.0)
[INFO] 1014360 WNER298 (6820.0, 6850.0)
[INFO] 1019246 WNTB302 (6820.0, 6830.0)
[INFO] 2987067 WQII643 (6820.0, 6850.0)
[INFO] 3263942 WQNH851 (6820.0, 6830.0)
[INFO] 3225580 WQMK438 (6820.0, 6830.0)
[INFO] 1010102 WNEG570 (6820.0, 6830.0)
[INFO] 1010095 WNEG561 (6820.0, 6830.0)
[INFO] 967725 KZK54 (6820.0, 6830.0)
[INFO] 1045355 WPNL762 (6820.0, 6830.0)
[INFO] 1010106 WNEG574 (6820.0, 6830.0)
[INFO] 1017720 WNEX908 (6820.0, 6830.0)
[INFO] 959756 KIB62 (6820.0, 6830.0)
[INFO] 961545 KLR22 (6820.0, 6830.0)
[INFO] 2693742 WQCE462 (6820.0, 6830.0)
[INFO] 970591 WBD40 (6823.75, 6827.5)
[INFO] 1051454 WPOU851 (6823.75, 6827.5)
[INFO] 2917935 WQHA306 (6827.5, 6830.0)
[INFO] 1935118 WPQZ738 (6828.875, 6832.375)
[INFO] 1015505 WNET572 (6828.875, 6832.375)
[INFO] 3958982 WRAA590 (6830.0, 6840.0)
[INFO] 968562 WAH765 (6830.0, 6860.0)
[INFO] 1019246 WNTB302 (6830.0, 6860.0)
[INFO] 1008622 WMW260 (6830.0, 6860.0)
[INFO] 957622 KCK21 (6830.0, 6860.0)
[INFO] 4135535 WRDB449 (6830.0, 6860.0)
[INFO] 1050681 WPOT822 (6830.0, 6840.0)
[INFO] 961542 KLQ99 (6830.0, 6860.0)
[INFO] 1012488 WNEN348 (6830.0, 6860.0)
[INFO] 3031174 WQJC397 (6830.0, 6860.0)
[INFO] 4345220 WRJS601 (6830.0, 6860.0)
[INFO] 2693740 WQCE460 (6830.0, 6840.0)
[INFO] 962905 KOR41 (6830.0, 6840.0)
[INFO] 1014344 WNER276 (6830.0, 6860.0)
[INFO] 1013286 WNEP339 (6830.0, 6840.0)
[INFO] 2544166 WPYK539 (6830.0, 6840.0)
[INFO] 2730252 WQCZ335 (6830.0, 6860.0)
[INFO] 3972483 WRAG353 (6830.0, 6860.0)
[INFO] 980226 WHC362 (6830.0, 6840.0)
[INFO] 1017710 WNEX898 (6833.35, 6856.65)
[INFO] 3842226 WQYD483 (6833.35, 6856.65)
[INFO] 3895214 WQYY354 (6833.35, 6856.65)
[INFO] 2609840 WPZY562 (6837.5, 6842.5)
[INFO] 1012483 WNEN343 (6840.0, 6850.0)
[INFO] 969456 WAU62 (6840.0, 6850.0)
[INFO] 3085218 WQKA838 (6840.0, 6850.0)
[INFO] 1051453 WPOU850 (6840.0, 6850.0)
[INFO] 1014360 WNER298 (6840.0, 6870.0)
[INFO] 1098733 WPQR462 (6840.0, 6850.0)
[INFO] 2529365 WPXZ277 (6840.0, 6870.0)
[INFO] 1024740 WNTM696 (6840.0, 6850.0)
[INFO] 1098735 WPQR464 (6840.0, 6850.0)
[INFO] 2730252 WQCZ335 (6840.0, 6870.0)
[INFO] 2908180 WQGW463 (6840.0, 6870.0)
[INFO] 978749 WGY620 (6840.0, 6850.0)
[INFO] 1045359 WPNL766 (6840.0, 6850.0)
[INFO] 2761940 WQDV530 (6840.0, 6850.0)
[INFO] 4096468 WRCL477 (6840.0, 6850.0)
[INFO] 2902742 WQGU424 (6840.0, 6850.0)
[INFO] 2415472 WPUZ454 (6840.0, 6850.0)
[INFO] 2961189 WQHV581 (6840.0, 6850.0)
[INFO] 4006631 WRAU216 (6840.0, 6850.0)
[INFO] 2978384 WQIF290 (6840.0, 6850.0)
[INFO] 1934952 WPQZ674 (6843.875, 6847.375)
[INFO] 1093208 WPQP307 (6847.5, 6852.5)
[INFO] 3049860 WQJK781 (6850.0, 6860.0)
[INFO] 3621571 WQUL311 (6850.0, 6860.0)
[INFO] 3263934 WQNH848 (6850.0, 6860.0)
[INFO] 1014344 WNER276 (6850.0, 6860.0)
[INFO] 2961189 WQHV581 (6850.0, 6860.0)
[INFO] 2543863 WPYK380 (6858.875, 6862.375)
[INFO] 969965 WAZ571 (6860.0, 6870.0)
[INFO] 969767 WAY534 (6860.0, 6870.0)
[INFO] 2415432 WPUZ428 (6860.0, 6870.0)
[INFO] 2996545 WQIM687 (6860.0, 6870.0)
[INFO] 3226608 WQMK883 (6860.0, 6870.0)
[INFO] 3263934 WQNH848 (6860.0, 6870.0)
[INFO] 960012 KIR67 (6860.0, 6870.0)
[INFO] 959285 KGM46 (6860.0, 6870.0)
[INFO] 1019246 WNTB302 (6860.0, 6870.0)
[INFO] 1023333 WNTK598 (6860.0, 6870.0)
[INFO] 1000736 WMF325 (6862.5, 6887.5)
[INFO] 1169185 WPQT500 (6862.5, 6887.5)
[INFO] 956036 KB55241 (6862.5, 6887.5)
[INFO] 960730 KK4462 (6862.5, 6887.5)
[INFO] 1008330 WMV760 (6862.5, 6887.5)
[INFO] 1007672 WMU890 (6862.5, 6887.5)
[INFO] 1008331 WMV761 (6862.5, 6887.5)
[INFO] 1169184 WPQT499 (6862.5, 6887.5)
[INFO] 956038 KB55244 (6862.5, 6887.5)
[INFO] 954891 KA4515 (6862.5, 6887.5)
[INFO] 2543863 WPYK380 (6863.875, 6867.375)
[INFO] 956415 KB97921 (6866.5, 6883.5)
[INFO] 1012500 WNEN360 (6867.5, 6872.5)
[INFO] 3357990 WQOY796 (6869.0, 6881.0)

also attaching the logs in info mode. out_AFCS.SIP.2_2024-01-19 20:40:49.889922.zip

AEgbert commented 10 months ago

The scope of this issue is far outside of what I'm able to reasonably help with. However, I've attempted to check your log file, and I think the issue may be as simple as something being wrong with how you're identifying the constraints to report. For the band you mentioned (5930-5949), your attached log file contains analysis for a few incumbents with more restrictive limits than the one you report. For instance, UUID 2730990 with callsign WQCZ751 is listed in your logs as existing on 5930.2-5960.2 with a psd_dbm_pre_Mhz of 11.04. This is both lower than the restraint you report (15.78) and satisfies the mask upper bound of 11.1.

If this does not help track down the issue, then I'm afraid I do not have much else to offer in the way of troubleshooting.

alexcpn commented 10 months ago

However, I've attempted to check your log file,

Thanks @AEgbert and much appreciated. I was not splitting the frequencies that were occurring in between other frequency ranges. I corrected that, and most frequency ranges looks good except for some restrictive frequency the most being the one below

[INFO] 5959.85-5989.85  psd: -7.0297487406666335

Contributed by the incumbent at uuid 2609844 and call_sign WPZY566 for the frequency range (5959.85, 5989.85)

[INFO] ********************************************************************************
[INFO] Going to process UUID = 2609844 call_sign = 'WPZY566
[INFO] accesspoint_loc =(38.377266, -78.468021) receiver_loc=(37.78341666666667, -78.49219444444445)
[INFO] Processing UUID = 2609844 call_sign = 'WPZY566' R to AP Dist =65950.19920028084 m, CF =5974.85 Mhz r_antenna_make= Commscope r_antenna_model=VHLP6-6WB (CAT A)
[INFO] uuid: 2609844
[INFO] call_sign: WPZY566
[INFO] frequency_assigned: 5974.85
[INFO] receiver_loc: (37.78341666666667, -78.49219444444445)
[INFO] t_loc_num: 1
[INFO] t_path_num: 3
[INFO] t_ant_num: 3
[INFO] receiver_to_ap_dist_m: 65950.19920028084
[INFO] r_height_to_center_raat: 29.6
[INFO] r_gain: 39.3
[INFO] r_diversity_gain: None
[INFO] r_azimuth: 0.0
[INFO] r_antenna_make: Commscope
[INFO] r_antenna_model: VHLP6-6WB (CAT A)
[INFO] r_loc_num: 4
[INFO] r_path_num: 3
[INFO] r_ant_num: 1
[INFO] polarization_code: None
[INFO] line_loss: None
[INFO] r_diameter_ft: 6.0
[INFO] r_diameter_m: 1.83
[INFO] ant_category: B2
[INFO] transmitter_loc: (37.98233333333334, -78.48236111111112)
[INFO] t_height_to_center_raat: 18.3
[INFO] t_gain: 41.3
[INFO] t_diversity_gain: None
[INFO] t_azimuth: 182.2
[INFO] t_antenna_make: Commscope
[INFO] t_antenna_model: UHX8-59J RF
[INFO] bandwidth: 30.0
[INFO] frequency_range: (5959.85, 5989.85)
[INFO] accesspoint_loc: (38.377266, -78.468021)
[INFO] accesspoint_ht_m: 300.0
[INFO] accesspoint_vtunc_m: 30
[INFO] Free Space Path Loss =144.3526345530228
[INFO] accesspoint_elev=528.5480346679688 receiver_elev=124.12525177001953 transmitter_elev=434.4264221191406
[INFO] GRx = 39.3 dB
[INFO] centerFreq of Receiver =5974.85 Mhz
[INFO] ant_category = B2 
[INFO] Angle 1: Azimuthal discrimination angle:R-AP The azimuthal discrimination angle,  θAZ_Disc= -0.40356838949331353 degress
[INFO] Angle 2: Elevation angle: R-T The receive antenna boresight elevation angle (from transmmitter) θEL_Disc= 0.7008329223071744 degrees
[INFO] Angle 3:elevation angle: R-AP The elevation angle from the  receive antenna to the evaluation phi= 0.36398860170787045 degrees
[INFO] --------------------------------------------------------------------------------
[INFO] Overall discrimination angle, 𝛾 =0.5256586433513493 degress
[INFO] --------------------------------------------------------------------------------
[INFO] 2.a GRx=39.3 >= 38:Calcualted:rpe=38.382383293689436 rpe-GRx=-0.9176167063105609
[INFO] 2.a GRx=39.3 >= 38:Calcualted:rpe=38.382383293689436 rpe-GRx=-0.9176167063105609
[INFO] getRPE gamma=-0.40356838949331353 ant_category=B2 rpe_p=-0.9176167063105609 rpe_n=-0.9176167063105609
[INFO] --------------------------------------------------------------------------------
[INFO] The azimuthal discrimination= GAZ-Disc =-0.9176167063105609 This should be negative
[INFO] The elevation discrimination  GEL-Disc in dB = -0.9176167063105609 contribution This should be Negative!)
[INFO] --------------------------------------------------------------------------------
[INFO] GRx_Effectivein dB = 35.382383293689436 [39.3 -0.5408482775876811] [-0.3767684287228799- polarization mismatch=3]
[INFO] ----GRx_Effective in dB = 35.382383293689436
[INFO] Near field adjustments do not have to be done
[INFO] Elevation Profile and ITM losss caluclated  in 0.000349 seconds
[INFO] ITM loss is 140.9460739798118
[INFO] Path Loss used =144.3526345530228
[INFO] building_entry_loss = 0 ap_indoor=False
[INFO] interference = -108.97025125933337 
[INFO] transmit_power PSD = -7.0297487406666335 dBm/MHz
[[INFO] Calculated Frequency
[INFO] 5925.0-5930.025  psd: 23.0 - Ok

Expected:5930-5949,upperBound:11.1 nominalValue:9.1
Expected:5949-6050,upperBound:11.2 nominalValue:9.2

[INFO] 5930.025-5959.85  psd: 9.21919183642514 ok
**[INFO] 5959.85-5989.85  psd: -7.0297487406666335 Not ok**
[INFO] 5989.85-5990.025  psd: 9.21919183642514 ok 
[INFO] 5990.025-6049.325  psd: 9.305186061950664 ok

Expected:6050-6078,upperBound:23.0 nominalValue:23.0

[INFO] 6049.325-6078.45  psd: 17.752804037801184 ok

Expected:6078-6109,upperBound:-0.4 nominalValue:-2.4

[INFO] 6078.45-6108.45  psd: -5.2792661211694565 -ok 

Expected:6109-6137,upperBound:23.0 nominalValue:23.0

[INFO] 6108.45-6108.625  psd: 17.911661857844592 ok
[INFO] 6108.625-6137.75  psd: 23.0  ok

Expected: 6137-6168,upperBound:-0.3 nominalValue:-2.3

[INFO] 6137.75-6167.75  psd: -5.178939698339647 - ok

Expected: 6168-6211,upperBound:23.0 nominalValue:23.0

[INFO] 6167.75-6167.925  psd: 23.0  ok
[INFO] 6167.925-6182.065  psd: 23.0  ok
[INFO] 6182.065-6211.89  psd: 23.0 ok

Expected: 6211-6241,upperBound:14.3 nominalValue:12.3

[INFO] 6211.89-6241.365  psd: 9.685669535605086  ok

 Expected: 6241-6302,upperBound:7.1 nominalValue:5.1

[INFO] 6241.365-6241.54  psd: 5.387537009608948  ok
[INFO] 6241.54-6271.54  psd: 5.366979961325029  ok
[INFO] 6271.54-6301.365  psd: 5.387537009608948  ok

Expected: 6302-6331,upperBound:17.2 nominalValue:15.2

[INFO] 6301.365-6330.84  psd: 11.794109109088566 ok

Expected: 6331-6359,upperBound:20.9 nominalValue:18.9

[INFO] 6330.84-6359.965  psd: 23.0 ok (3 decibel mismatch could be polarization mismatch)

Expected: 6359-6392,upperBound:18.2 nominalValue:16.2
Expected:6392-6420,upperBound:18.3 nominalValue:16.3

[INFO] 6359.965-6419.965  psd: 13.26858071224217 ok

Expected: 6420-6425,upperBound:23.0 nominalValue:23.0

[INFO] 6419.965-6425.0  psd: 23.0 ok

Expected: 6525-6540,upperBound:23.0 nominalValue:23.0

[INFO] 6525.0-6540.0  psd: 23.0 ok

Expected: 6540-6570,upperBound:23.0 nominalValue:22.9

[INFO] 6540.0-6570.0  psd: 15.737189287409763 ok

Expected:6570-6580,upperBound:23.0 nominalValue:23.0

[INFO] 6570.0-6580.0  psd: 23.0 ok

Expected:6580-6590,upperBound:16.6 nominalValue:14.6

INFO] 6580.0-6590.0  psd: 23.0 - Not okay check

Expected:6590-6600,upperBound:23.0 nominalValue:22.7

[INFO] 6590.0-6600.0  psd: 19.745014053790953  ok

Expected:6600-6610,upperBound:23.0 nominalValue:23.0

[INFO] 6600.0-6610.0  psd: 20.228282029739717 ok

Expected:6610-6640,upperBound:17.1 nominalValue:15.1

[INFO] 6610.0-6640.0  psd: 11.105362298937578 ok

Expected:6640-6650,upperBound:23.0 nominalValue:23.0

[INFO] 6640.0-6650.0  psd: 21.407024789643486 ok

[INFO] 6650.0-6660.0  psd: 0 ok

**[INFO] 6660.0-6670.0  psd: -5.066111165548648 - not okay**
[INFO] 6670.0-6675.2  psd: 0  okay

Expected:6676-6680,upperBound:19.9 nominalValue:17.9 
6680-6694,upperBound:23.0 nominalValue:21.3
6694-6700,upperBound:23.0 nominalValue:21.4

[INFO] 6675.2-6700.0  psd: 23.0  - not okay (3 decibel change could satisfy this)

Expected:6700-6710,upperBound:15.4 nominalValue:13.4

[INFO] 6700.0-6710.0  psd: 10.891977330796422 ok

Expected:6710-6770,upperBound:23.0 nominalValue:23.0

[INFO] 6710.0-6740.0  psd: 20.96310494836436  ok
[INFO] 6740.0-6770.0  psd: 21.021096888872478 ok

Expected:6770-6800,upperBound:20.0 nominalValue:18.0

**[INFO] 6770.0-6800.0  psd: 8.081817084848169** ok but too low

Expected:6800-6803,upperBound:23.0 nominalValue:23.0

[INFO] 6800.0-6803.75  psd: 23.0 ok

Expected:6803-6808,upperBound:12.8 nominalValue:10.8

[INFO] 6803.75-6807.5  psd: 8.015087793907895 ok

Expected:6808-6810,upperBound:23.0 nominalValue:23.0

[INFO] 6807.5-6817.5  psd: 23.0 ok

Expected:6810-6820,upperBound:23.0 nominalValue:21.0
6820-6850,upperBound:23.0 nominalValue:23.0

[INFO] 6817.5-6822.5  psd: 22.722624097951012 ok

Expected:6850-6860,upperBound:8.8 nominalValue:6.8

**[INFO] 6822.5-6860.0  psd: 23.0 not okay**

Expected:6860-6870,upperBound:17.1 nominalValue:15.1

[INFO] 6860.0-6870.0  psd: 12.10214734191419 okay

Expected:6870-6875,upperBound:23.0 nominalValue:23.0

[INFO] 6870.0-6875.0  psd: 23.0 okay
AEgbert commented 10 months ago

Looking at that specific incumbent, I think it may be a matter of the terrain profile used for ITM path loss calculations. Looking at the terrain data between those locations, I believe there is a significant elevation change between the AFC device and the incumbent receiver that blocks line of sight. If this terrain profile is given to ITM, I expect you'll get significantly more path loss than you currently have.

alexcpn commented 10 months ago

Terrain profile is given to the ITM in these calculations. Since we have to take the most restrictive PSD, I believe we need to take the max path loss ,

For example if I give the elevation data from access-point to receiver I get the below restrictive PSD

elevation_data = libneddata.getElevationData(accesspoint_loc,receiver_loc, dataset,rTreeIndx)
accesspoint_ht_asml = accesspoint_elv[0] +accesspoint_ht_m
itmloss= itmobj.getItmLoss(center_freq, r_height_to_center_raat,accesspoint_ht_asml, elevation_data, distance_m)
[INFO] 5959.85-5989.85  psd: -7.0297487406666335 - Not ok
[INFO] 5989.85-6019.15  psd: 23.0
[INFO] 6019.15-6049.15  psd: 21.070465485776083
[INFO] 6049.15-6078.45  psd: 23.0
[INFO] 6078.45-6108.45  psd: -5.2792661211694565 - ok
[INFO] 6108.45-6137.75  psd: 23.0
[INFO] 6137.75-6167.75  psd: -5.178939698339647 -ok

If I do the reverse, that is give from receiver to access point 5959.85-5989.85 becomes okay but the other two ranges are calculated wrongly

elevation_data = libneddata.getElevationData(receiver_loc,accesspoint_loc, dataset,rTreeIndx)
accesspoint_ht_asml = accesspoint_elv[0] +accesspoint_ht_m
itmloss= itmobj.getItmLoss(center_freq, r_height_to_center_raat,accesspoint_ht_asml, elevation_data, distance_m)
[INFO] 5959.85-5989.85  psd: 23.0 ok
[INFO] 5989.85-6019.15  psd: 23.0
[INFO] 6019.15-6049.15  psd: 23.0
[INFO] 6049.15-6078.45  psd: 23.0
[INFO] 6078.45-6108.45  psd: 23.0 Not ok
[INFO] 6108.45-6137.75  psd: 23.0
[INFO] 6137.75-6167.75  psd: 23.0 Not ok
...
AEgbert commented 10 months ago

I think the terrain obstruction may be narrow along the path between the incumbent receiver and AFC device. It's possible that your elevation database does not have sufficient resolution, or your elevation profile is too sparsely sampled to capture the terrain obstruction accurately. As best as I can tell, I expect the incumbent you've identified here likely does not impact the test vector mask values because of a terrain obstruction blocking the line-of-sight path between incumbent receiver and AFC device.

alexcpn commented 10 months ago

Thanks, @AEgbert for this very helpful hint.

Since the line between the AP and Receiver was crossing multiple NED files, I had segmented it into multiples lines and was reading from each. However there was some complexity related to an index for each line which caused the sort order of the line segments to be not incorrect and hence the order of assembling the elevation points was wrong. Which when I correct is giving higher ITM values. I am trying to correct this for all cases and run SIP.2

https://colab.research.google.com/drive/1zWU4l7OwD7UaZBQmB_NZOwpKEwenklMP?usp=sharing

alexcpn commented 10 months ago

@AEgbert even with the correction int the elevation there seems to be low loss coming from ITM. I am using the WINNF ITM library

Adding also @sergebdt

https://github.com/Wireless-Innovation-Forum/Spectrum-Access-System/tree/master/src/harness/reference_models/propagation/itm

It seems there is some problem with the WINNF ITM library on how it is sampling the elevation data.

Irrespective of the number of samples it is missing the elevation in the middle between Access Point and Microwave Receiver

(Incidentally the AP height over ground seems to be very high here at 300 meters)

Case 1

I took around 20,000 samples. The data is from the NED files at this location https://github.com/Wireless-Innovation-Forum/Common-Data/tree/master/data/ned so that it should have the specified details

[INFO] Total profiles collected =19994 [DEBUG] Total distance of segments =66.32 km height1 (AP asml)=828.5480346679688 height2(Receiver asml)=153.72525177001953 frequency=5989.85 distance_m=65950.19920028084 [DEBUG] Loss=140.97252928518384 For reliability=0.2 and confidence=0.05 Calculated itmloss=140.97252928518384

This is the plotted elevation data.

image

Case 2

Reduced sample to around 200 for easy debugging and also finding that increasing the sampling is not really helping

image

height1 (AP asml)=828.5480346679688 height2(Receiver asml)=153.72525177001953 frequency=5989.85 distance_m=65950.19920028084 [DEBUG] Loss=140.97251889952966 For reliability=0.2 and confidence=0.05 Calculated itmloss=140.97251889952966

Elevation profile seems correct; I checked this via the NED files directly https://colab.research.google.com/drive/1zWU4l7OwD7UaZBQmB_NZOwpKEwenklMP?usp=sharing

elevation_data=[528.55, 509.59, 460.06, 416.5, 344.12, 379.54, 445.67, 433.08, 476.71, 424.14, 389.1, 346.34, 341.0, 326.85, 332.98, 341.35, 301.15, 279.21, 260.45, 297.33, 292.16, 304.74, 308.95, 335.83, 369.83, 388.8, 391.25, 387.64, 344.69, 261.0, 244.73, 243.21, 255.28, 231.14, 220.78, 210.35, 213.1, 213.93, 212.09, 206.41, 213.67, 207.85, 216.89, 209.2, 209.57, 213.78, 206.95, 194.69, 197.65, 187.21, 183.44, 186.1, 190.79, 203.59, 201.12, 228.57, 235.99, 197.77, 181.73, 190.39, 187.8, 180.79, 186.31, 174.58, 179.29, 171.07, 174.02, 170.2, 160.1, 156.66, 163.63, 147.37, 145.18, 152.57, 171.11, 170.38, 176.82, 170.15, 164.97, 160.05, 159.8, 143.22, 137.15, 127.88, 136.01, 127.77, 149.18, 131.22, 125.76, 142.64, 151.41, 147.27, 165.04, 168.0, 142.22, 151.45, 167.58, 183.66, 177.04, 176.55, 185.88, 175.0, 188.81, 190.24, 179.0, 173.43, 189.19, 184.34, 169.22, 161.59, 158.31, 171.06, 172.89, 185.83, 190.02, 178.86, 184.28, 182.0, 190.06, 195.31, 187.64, 176.58, 169.06, 179.16, 172.78, 162.35, 167.12, 160.01, 151.36, 137.77, 142.04, 149.17, 154.21, 143.89, 153.05, 159.25, 163.49, 164.94, 164.25, 166.62, 158.3, 158.71, 158.03, 143.9, 147.7, 146.36, 147.32, 132.75, 118.66, 175.4, 176.55, 180.7, 172.58, 176.27, 155.26, 158.33, 147.96, 144.62, 134.14, 129.21, 127.53, 123.79, 119.99, 120.41, 160.72, 166.76, 166.6, 153.43, 140.87, 126.94, 135.79, 127.99, 138.49, 145.07, 141.33, 133.61, 136.6, 133.54, 130.81, 135.81, 147.29, 138.99, 140.1, 131.39, 120.82, 129.46, 135.44, 133.24, 126.54, 116.24, 107.15, 103.11, 136.99, 149.3, 147.64, 130.5, 117.92, 120.35, 115.06, 117.11, 121.95, 139.12, 130.64, 138.91, 146.78, 147.05, 173.17, 216.87, 240.68, 239.26, 318.38, 325.79, 320.26, 379.1, 406.28, 422.43, 403.52, 362.25, 332.99, 326.5, 316.2, 312.13, 288.0, 262.02, 256.82, 233.52, 212.05, 219.76, 224.28, 210.02, 213.08, 202.51, 188.36, 195.41, 194.69, 184.5, 192.12, 189.67, 183.25, 182.08, 187.09, 191.61, 205.28, 208.55, 193.51, 182.28, 170.25, 158.78, 153.59, 155.58, 182.75, 190.77, 182.23, 174.69, 170.06, 157.9, 150.06, 146.25, 140.0, 141.69, 152.6, 155.29, 146.64, 142.0, 138.87, 150.52, 144.5, 152.78, 156.46, 151.22, 139.71, 142.36, 146.9, 141.82, 140.98, 136.49, 131.83, 133.47, 146.59, 151.79, 139.89, 146.88, 155.9, 158.98, 158.58, 159.43, 152.51, 147.17, 144.76, 149.22, 139.71, 133.45, 141.39, 145.33, 145.27, 139.36, 139.21, 133.57, 122.64, 127.36, 136.57, 126.99, 133.83, 135.22, 139.66, 135.22, 134.9, 130.77, 133.86, 135.9, 123.96, 121.6, 113.08, 106.04, 103.34, 103.76, 101.96, 105.28, 106.81, 106.21, 106.44, 106.13, 106.64, 103.69, 105.82, 105.79, 105.39, 103.51, 104.03, 105.61, 125.42, 124.66, 132.69, 131.09, 129.19, 123.44, 134.24, 128.76, 122.86, 125.02, 120.78, 122.21, 129.81, 131.85, 129.45, 121.06, 124.46, 132.12, 138.44, 143.51, 141.67, 142.95, 139.68, 139.84, 137.84, 137.2, 137.4, 131.93, 134.62, 136.24, 134.07, 136.81, 135.07, 131.87, 130.65, 136.54, 138.25, 133.74, 123.85, 117.66, 119.76, 121.45, 118.23, 120.62, 119.72, 113.4, 98.31, 112.73, 118.11, 108.41, 90.84, 82.74, 78.21, 82.29, 85.26, 84.83, 85.23, 113.7, 110.01, 120.4, 122.84, 124.85, 120.03, 105.35]

Case 3

200 sample still. But instead of giving the sampling as per Winnf ITM https://github.com/Wireless-Innovation-Forum/Spectrum-Access-System/blob/master/src/harness/reference_models/propagation/itm/itm_test.py#L88-L89

if I change from elevation_data.insert(1,distance_m/num_of_profile) to elevation_data.insert(1,num_of_profile)

  def getItmLoss(self, frequency, height1,height2, elevation_data, distance_m):
    confidence_vals = [0.05]
    reliability_vals = [0.2]

    refractivity = 314.0  # Typical
    refractivity_final=True
    dielectric = 25  # WINNF
    conductivity = 0.02  # WINNF
    climate = 5  # Continental temperate
    polarization = 0  # WINNF
    mdvar = 13 #WINNF

    num_of_profile = len(elevation_data)-1
    elevation_data.insert(0,num_of_profile)
    elevation_data.insert(1,distance_m/num_of_profile) # change sampling here

    loss, ver0, ver1, err, mode = itm.point_to_point(elevation_data, height1, height2,
                                             dielectric, conductivity, refractivity,
                                             frequency, climate, polarization,
                                             confidence_vals[0], reliability_vals[0],
                                             mdvar, refractivity_final)

to

  def getItmLoss(self, frequency, height1,height2, elevation_data, distance_m):
...
    num_of_profile = len(elevation_data)-1
    elevation_data.insert(0,num_of_profile)
    elevation_data.insert(1,num_of_profile) # only change here

    loss, ver0, ver1, err, mode = itm.point_to_point(elevation_data, height1, height2,
                                             dielectric, conductivity, refractivity,
                                             frequency, climate, polarization,
                                             confidence_vals[0], reliability_vals[0],
                                             mdvar, refractivity_final)

And test, I am getting the higher path loss. which seems consistent with the elevation in the middle

height1 = accesspoint_ht_asml
height2 = receiver_ht_asml
frequency =frequency_assigned + bandwidth_Mhz/2
distance_m = distance.distance(accesspoint_loc, receiver_loc).m
print(f"height1 (AP asml)={height1} height2(Receiver asml)={height2} frequency={frequency} distance_m={distance_m}")

itmloss= itmobj.getItmLoss(frequency, height1,height2, elevation_data, distance_m)
print(f"Calculated itmloss={itmloss}")

output with this change 

height1 (AP asml)=828.5480346679688 height2(Receiver asml)=153.72525177001953 frequency=5989.85 distance_m=65950.19920028084
[DEBUG] Loss=171.20184959228385 For reliability=0.2 and confidence=0.05
Calculated itmloss=171.20184959228385

However when I do this the WINNF ITM test case fails

Before

def getItmLossClassic(self, frequency, height1,height2, elevation_data, distance_m):
    # https://github.com/Wireless-Innovation-Forum/Spectrum-Access-System/blob/19f2ae9c1a1844e2f2ca2ceddd07f392249741e6/src/harness/reference_models/propagation/itm/itm_test.py#L201
    confidence_vals = [0.5, 0.9, 0.1]
    reliability_vals = [0.01, 0.1, 0.5, 0.9, 0.99]
    expected_losses = [128.6, 137.6, 119.6,
                       132.2, 140.8, 123.5,
                       135.8, 144.3, 127.2,
                       138.0, 146.5, 129.4,
                       139.7, 148.4, 131.0]

    refractivity = 314.0  # Typical
    refractivity_final=True
    dielectric = 15  # Typical for ground
    conductivity = 0.005  # Typical for ground
    climate = 5  # Continental temperate
    polarization = 0  # Vertical
    mdvar = 12
    k = 0

    num_of_profile = len(elevation_data)-1
    elevation_data.insert(0,num_of_profile)
    elevation_data.insert(1,distance_m/num_of_profile)

    loss, ver0, ver1, err, mode = itm.point_to_point(elevation_data, height1, height2,
                                            dielectric, conductivity, refractivity,
                                            frequency, climate, polarization,
                                            confidence_vals[k], reliability_vals[k],
                                            mdvar, refractivity_final)

    log.debug(f"Loss={loss} For reliability={reliability_vals[0]} and confidence={confidence_vals[0]} Excpected={expected_losses[k]} ")
    return loss

Output before this change (note I have used same parameters here for conductivity, climate etc as per the test)

[DEBUG] Loss=128.59671741291228 For reliability=0.01 and confidence=0.5 Excpected=128.6 
Reference itmloss=128.59671741291228 Expected  128.6,

After this sampling change elevation_data.insert(1,num_of_profile) Output is

[DEBUG] Loss=104.43987979352246 For reliability=0.01 and confidence=0.5 Excpected=128.6 
Reference itmloss=104.43987979352246 Expected  128.6,
alexcpn commented 10 months ago

Adding more data poing to the above comment

This the plot of elevation from code (orange) as well as directly from flt files (blue). The shift is due to the slight difference in the number of samples. You can see that the elevation profile is correct

image

elevation_data =libneddata.getElevationData(accesspoint_loc,receiver_loc, dataset, rTreeIndx)
elevation_data_flt =[124.12525, 100.833755, 122.87143, 123.32976, 122.25566, 116.57078, 113.098114, 92.95955, 84.76377, 85.25844, 82.29317, 78.211, 84.139244, 94.50441, 114.6446, 116.38137, 91.57068, 108.777626, 117.93253, 120.62623, 115.811646, 121.44716, 118.21726, 117.66227, 127.767624, 134.58095, 139.73706, 134.19139, 130.06195, 133.23605, 137.42639, 134.57881, 137.16936, 134.62318, 131.9257, 137.39694, 137.5365, 138.09904, 140.60214, 141.7445, 141.29504, 143.491, 140.19255, 133.22461, 126.21056, 121.18165, 129.44571, 131.84729, 129.87477, 118.70335, 114.36087, 111.58925, 126.687515, 130.70837, 128.09668, 127.502594, 130.65497, 132.68976, 124.65571, 125.42334, 104.34001, 103.77601, 104.2924, 105.94702, 105.737816, 104.415276, 106.558556, 106.36363, 106.29706, 106.21269, 106.81254, 105.2823, 103.62328, 103.680305, 103.797035, 106.545105, 113.30708, 123.115135, 134.19049, 131.40019, 135.3478, 134.15105, 134.48036, 138.29338, 135.21515, 133.82773, 126.99318, 137.65463, 126.04518, 125.359085, 135.36432, 140.8913, 143.90768, 147.02615, 143.1508, 133.45164, 140.1042, 149.21631, 146.32664, 151.44887, 157.20859, 156.17828, 159.6626, 156.55238, 149.5168, 139.49869, 148.97055, 146.28299, 133.47037, 131.82779, 137.48477, 139.68364, 139.69511, 135.83182, 146.0718, 146.0602, 155.16313, 154.8511, 145.12842, 150.52026, 138.87364, 141.99965, 149.1689, 155.13965, 149.38066, 146.18883, 145.2314, 145.35394, 151.66586, 164.68205, 171.76529, 182.22597, 190.76904, 182.75223, 148.39122, 157.92696, 162.20721, 177.83205, 188.10336, 201.58597, 206.74684, 197.94937, 188.31062, 182.07628, 183.24515, 189.66829, 188.60304, 189.0584, 198.20969, 189.43921, 195.06114, 209.47263, 214.46292, 219.28397, 222.89648, 212.05057, 233.52068, 256.82208, 265.28128, 295.27252, 314.53525, 323.8624, 326.19156, 347.7797, 391.17206, 428.6666, 412.21664, 379.09506, 320.2592, 325.79495, 306.77194, 225.79083, 238.39388, 189.30077, 160.81693, 146.77458, 141.68326, 133.56323, 137.36217, 121.9539, 117.113, 115.06378, 115.125885, 131.68584, 147.57193, 149.36275, 133.64546, 107.36067, 102.74581, 111.175224, 125.58456, 132.54369, 133.4254, 126.63867, 121.476875, 132.5058, 140.6283, 138.98663, 138.7043, 126.17729, 136.56877, 139.00102, 127.63369, 137.28764, 145.84021, 146.50021, 127.68576, 135.10484, 132.63043, 142.63118, 153.66516, 165.19395, 165.92679, 159.0313, 118.095146, 121.47743, 125.197784, 137.82645, 131.30836, 132.0448, 143.72073, 146.05348, 159.53864, 161.5898, 178.34834, 177.4879, 181.87839, 174.70403, 118.66201, 123.06152, 135.80666, 152.21089, 143.12, 146.86064, 158.05229, 159.5616, 158.30261, 168.9262, 165.27754, 165.14244, 161.4503, 158.17316, 149.45514, 148.30988, 141.40565, 136.90623, 140.68982, 138.2744, 154.2604, 165.60655, 157.46872, 168.18571, 179.11475, 172.61469, 173.37721, 183.95358, 194.35205, 189.1427, 182.67577, 185.926, 182.78856, 190.7787, 181.04074, 165.28188, 173.44481, 157.09763, 169.65976, 176.0161, 189.09868, 186.65062, 174.31212, 183.43457, 195.01167, 181.107, 182.12755, 184.42534, 181.45238, 183.65897, 170.57443, 145.47395, 142.22003, 168.9296, 163.51463, 141.5477, 150.10458, 142.00752, 127.125, 138.26352, 143.05634, 132.3408, 127.80176, 129.5601, 130.70212, 151.9897, 150.96178, 157.50563, 168.70924, 174.09769, 172.3484, 173.24623, 156.62523, 145.18, 151.22212, 163.38283, 151.75403, 161.00227, 161.11441, 176.282, 175.48505, 177.99936, 182.58914, 184.09074, 182.44847, 187.61534, 189.1336, 192.32312, 214.7823, 246.06497, 201.951, 195.11105, 194.98477, 186.10167, 183.92299, 187.2118, 197.64804, 194.68542, 208.84767, 208.5717, 205.77159, 215.32097, 212.35025, 207.3271, 219.8789, 201.33217, 211.47238, 206.82333, 218.4968, 217.91833, 228.84058, 251.39935, 231.6214, 241.77203, 257.31015, 324.91382, 387.22122, 389.8501, 391.06097, 370.10074, 327.71146, 307.17694, 298.9928, 280.64923, 293.98804, 262.95325, 286.88354, 327.39685, 343.61945, 322.5827, 342.6633, 335.7236, 369.96973, 408.69922, 476.5934, 441.9924, 451.03198, 393.18637, 340.55164, 416.5004, 460.0621, 509.58926, 528.54803]
print(f"len elevation_data= {len(elevation_data)} len elevation_data_flt = {len(elevation_data_flt)}")
elevation_data_disp = elevation_data.copy()
elevation_data_flt_disp = elevation_data_flt.copy()
# elevation_data_disp = [round(item,2) for item in elevation_data_disp]
# print(f"elevation_data={elevation_data_disp}")

height1 = accesspoint_ht_asml
height2 = receiver_ht_asml
frequency =frequency_assigned + bandwidth_Mhz/2
distance_m = distance.distance(accesspoint_loc, receiver_loc).m
print(f"height1 (AP asml)={height1} height2(Receiver asml)={height2} frequency={frequency} distance_m={distance_m}")

itmloss= itmobj.getItmLoss(frequency, height1,height2, elevation_data, distance_m)
print(f"Calculated itmloss={itmloss} with calculated elevation_data")

itmloss= itmobj.getItmLoss(frequency, height1,height2, elevation_data_flt, distance_m)
print(f"Calculated itmloss={itmloss} with elevation_data_flt")

# Plot the elevation profile
plt.plot(elevation_data_flt_disp, '-') # from flt files
plt.plot(elevation_data_disp, '-x') # from code
plt.xlabel('Samples')
#plt.xlim(0,(distance_m/len(elevation_data1))) #todo -plot with distance
plt.ylabel('Elevation (m)')
plt.title('Elevation Profile')
plt.show()
# Plot the elevation profile

Note that only when I switch elevation data getting from receiver_loc to accesspoint_loc

elevation_data =libneddata.getElevationData(receiver_loc.accesspoint_loc, dataset, rTreeIndx)
To
elevation_data =libneddata.getElevationData(accesspoint_loc,receiver_loc, dataset, rTreeIndx)

And using the full sampling do I get a higher loss. 

[DEBUG] Loss=171.20184959228385 For reliability=0.2 and confidence=0.05
Calculated itmloss=171.20184959228385 with calculated elevation_data (switched - orange plot)
Steps= 370
[DEBUG] Loss=147.07224249706726 For reliability=0.2 and confidence=0.05
Calculated itmloss=147.07224249706726 with elevation_data_flt # from NED files direct - see the ipynb notebook

image

What is the expected ITM loss between the two points. With anything around 140 dbm the frequency range (5959.85-5989.85 ) appears ; and since you mentioned

. As best as I can tell, I expect the incumbent you've identified here likely does not impact the test vector mask values because of a terrain obstruction blocking the line-of-sight path between incumbent receiver and AFC device.

and it is coming as per what you have said in the elevation plot, I am assuming that loss of > 140 is correct and hence all this debugging.

Note that if get this frequency range 5959.85-5989.85 correct, the same ITM loss also increases for 6078.45-6108.45 and 6137.75-6167.75

sergebdt commented 10 months ago

I got your ping. Note that I am no more active with Winnforum project, and no more working for Google who was major contributor to these code and library.

Your discussion is quite long, and I do not get exactly what is your issue, and what you expect. If you could summarize, perhaps I can help.

Please note several quick remarks for now:

I also see that in your examples, you are reinventing the wheel a lot, for example in relation to getting the profile between 2 points. I would advice to simply use the top function TerrainProfile() for that, as it has been extensively used and tested as part of the 3.5GHz SAS project. See: https://github.com/Wireless-Innovation-Forum/Common-Data/blob/694c98ff82b05c5404a8d67698074233456bfae1/src/winnf/geo/terrain.py#L28

See also for example here how the ITM model is used in contexte of SAS, with a few parameter tuning as defined by the SAS specification: https://github.com/Wireless-Innovation-Forum/Spectrum-Access-System/blob/19f2ae9c1a1844e2f2ca2ceddd07f392249741e6/src/harness/reference_models/propagation/wf_itm.py#L77

sergebdt commented 10 months ago

Also regarding your comment above where you change the method of calling the itm point_to_point() routine:

elevation_data.insert(1,distance_m/num_of_profile) to elevation_data.insert(1,num_of_profile)

This is not correct -- the profile that is passed is supposed to have the first 2 slots filled with: num_points-1 and distance_between_points_in_meters.

Note also that in your colab that you reference above, the way you build the profile is not correct: you basically sample a straight lines in the lat/long "projection" (which the tiles are since they are grid of samples separated by 1 arcsec in both lat and long axis). Instead of going around the geodesic.

Use instead the TerrainProfile() routine above that takes care of creating the geodesic, loading the tiles as needed and reading the elevation for the correctly space points along the geodesic.

If your issue is trying to fit your current way of sampling things with a reference that use a more correct method (along the lines just described), then do not look further for any discrepancies that you see.

alexcpn commented 10 months ago

Thanks for your comments @sergebdt and for responding. Lot of points to digest, but just a quick response from my side

This is not correct -- the profile that is passed is supposed to have the first 2 slots filled with: num_points-1 and distance_between_points_in_meters.

I am doing this in the function call internally

 num_of_profile = len(elevation_data)-1
    elevation_data.insert(0,num_of_profile)
    elevation_data.insert(1,distance_m/num_of_profile)

    loss, ver0, ver1, err, mode = itm.point_to_point(elevation_data, height1, height2,
                                            dielectric, conductivity, refractivity,
                                            frequency, climate, polarization,
                                            confidence_vals[k], reliability_vals[k],
                                            mdvar, refractivity_final)

the way you build the profile is not correct: you basically sample a straight lines in the lat/long "projection" (which the tiles are since they are grid of samples separated by 1 arcsec in both lat and long axis). Instead of going around the geodesic.

I guess you are right here. But in a distance of 200 km and with location uncertainty, a straight line between two points should capture the elevation. And it has for this instance caught the high elevation between the access point and receiver. I am doing like this for very fast access. Basically internally storing this in TensorStore/ZarrSrtore all the NED points and indexing out them. I am not defending this way here and I will try to test with the function you said, but even with my not fully accurate, the elevation is found.

The real code is doing like this using Shapely LineString.

        line = LineString([cord_1,cord_2])
        distance_m = distance.geodesic(cord_1[::-1],cord_2[::-1]).m
        total_distance += distance_m
        log.debug(f"The distance from from geopy.distance is {distance_m:.2f} m")
        # Interpolate along line segment to get sampling points
        num_samples = determine_num_samples(distance_m)
        steps = np.interp(range(num_samples), [0,num_samples], [0, line.length])
        point_geoms = [line.interpolate(currentdistance) for currentdistance in steps]
        # read in the transform for these points
        transform = transform_map[key]
        # read in the datasets for these points
        k1 = dataset_map[key]
        for point in point_geoms:
        # these lon,lat we calculate via Shapely from Source and Dest co-ordinates
         lon,lat =point.x, point.y
         x, y = rasterio.transform.rowcol(transform, lon, lat)
         v1 =k1[:,x,y][0]
         rofiles.append(v1) #

I also see that in your examples, you are reinventing the wheel a lot, for example in relation to getting the profile between 2 points. I would advice to simply use the top function TerrainProfile() for that, as it has been extensively used and tested as part of the 3.5GHz SAS project

Use instead the TerrainProfile() routine above that takes care of creating the geodesic, loading the tiles as needed and reading the elevation for the correctly space points along the geodesic

Since loading the NED files from disk one by one (and with caching) it is still pretty slow compared to reading from ZarrStore/TensorStore. This is all the more important as we need to search a 200 km radius with so many Microwar incumbents + antenna + frequency combination and possibly tens of thousands of wifi access points

Thanks again for you other points, I will check this and reply

sergebdt commented 10 months ago

But in a distance of 200 km and with location uncertainty, a straight line between two points should capture the elevation.

f you dont need to be precise but very fast, you can do your way by defining geodesic along the lat/long space as you do, although it will differ widely from another implementation that may use another method.

The precise vincenty method is quite slow by itself. A midground is to replace it by GreatCircle method that assumes that the earth is a sphere and not an ellipsoid, and are much faster (while keeping some precision) -- see for example the replacement routines here: https://github.com/Wireless-Innovation-Forum/Spectrum-Access-System/blob/19f2ae9c1a1844e2f2ca2ceddd07f392249741e6/src/lib/geo/geo_utils.py#L196

Regarding the general architecture, i dont have a specific recommendation as I dont know the context of your application : the tiling code mentioned earlier uses a simple strategy that was sufficient for the need of SAS test harness in a batch environment. It handles the requests by loading a limited number tiles in a memory cache in LRU (last recently used) fashion, with ability to specify how many tiles are kept in that memory.cache. Once a tile is in memory, the data access is reasonably fast. Each tile is 1degx1deg, ie approximately 100x100km. So 4 are typically used to go 200km in all directions.

The way to make it work faster in production batch environment is to: 1) select the appropriate memory to reserve for the cache, ie the number of tiles in the cache 2) make sure your calls are done with grouping of geographically close devices, so that loading of tiles into the cache is minimized. For example by spliting the space in blocks of size > 1 tile, then spanning the blocks from top-left to bottom-left, then bootom-next-right to top-next-right, etc..

Obviously if you have a more streaming environment, where request bubbles for devices randomly positioned everywhere, then a better architecture is probably required.

alexcpn commented 10 months ago

Thanks again for your comments.

I have taken your input and changed getting points from treating it as a straight line, to treating it as an ellipsoid. Thankfully pyproj Geod had the method for this https://pyproj4.github.io/pyproj/stable/api/geod.html

#Interpolate along the great circle treating earh as ellipsoid
geoid = Geod(ellps="WGS84")
point_geoms = geoid.npts(lon0, lat0, lon1, lat1, num_samples)

This is the QGIS plot for confirmation

image

Zooming over with the points which lie over the elevation

image

Also plotted this out like before, with the orange being the points along the great circle sampled every 177 meters,and the blue being the points along the straight line

image

True there are slight deviations, and I will use the method that you suggested which is also the most accurate, that is take the points along the great circle

In the code, I even sampled every 5 meters getting around 13256 points. Still, the max loss I am getting is around 140. (the graph shown here is with a lower sampling for better viewing)

    num_of_profile = len(elevation_data)-1
    distance_between_points =round(distance_m/num_of_profile)
    elevation_data.insert(0,num_of_profile)
    elevation_data.insert(1,distance_between_points)

    loss, ver0, ver1, err, mode = itm.point_to_point(elevation_data, height1, height2,
                                             dielectric, conductivity, refractivity,
                                             frequency, climate, polarization,
                                             confidence_vals[0], reliability_vals[0],
                                             mdvar, refractivity_final)

    log.debug(f"Loss={loss} For reliability={reliability_vals[0]} and confidence={confidence_vals[0]} stepsize= {distance_between_points}")
    return loss

output

[DEBUG] Elevation Profile 13256 read in 0:00:00.132086
len elevation_data= 13256 len elevation_data_flt = 159
height1 (AP asml)=828.5480346679688 height2(Receiver asml)=153.72525177001953 frequency=5989.85 distance_m=65912.87842494999
[DEBUG] Loss=140.97731331580064 For reliability=0.2 and confidence=0.05 stepsize= 4.972680379098453

I also tried to put a pseudo point before the hill for the receiver to see how much the path loss increases

accesspoint_loc =(38.377266, -78.468021) 
#receiver_loc=(37.78341666666667, -78.49219444444445) # true point
receiver_loc=(38.02730,-78.48281) # before the hill, just for test

image

and got the loss as 136 dB

height1 (AP asml)=828.5480346679688 height2(Receiver asml)=153.72525177001953 frequency=5989.85 distance_m=38867.92584756884
[DEBUG] Loss=136.38754035671604 For reliability=0.2 and confidence=0.05 stepsize= 177
Calculated itmloss=136.38754035671604 with calculated elevation_data
[DEBUG] Loss=136.38756178338937 For reliability=0.2 and confidence=0.05 stepsize= 177
Calculated itmloss=136.38756178338937 with elevation_data_flt

So the obstruction is adding just 4 dB to the loss

@AEgbert Is this 4 dB loss sufficient to account for the approximately 400-meter obstruction caused by the hill between the AP and receiver? If so then there is no point in checking the ITM loss calculation further.

sergebdt commented 9 months ago

140dB seems to be around the free space path loss for that distance and frequency. So you are basically saying there is no diffraction loss applied, or very little. First remark: the profile graph above does not show visually the Tx and Rx hieght above ground. If you take them into account, then you see that the hill is around the direct direction of Tx to Rx, on first approximation: 153 + (853-153) * 130 / 370 ~= 390, while the hill goes up to 420m, so not much above Note: internally there is compensation for earth curvature, if I remember well, so the calculation just mentioned is very rough.

If you want to assess exactly what is going on:

alexcpn commented 9 months ago

@sergebdt I was debugging by testing this with SAS code https://github.com/Wireless-Innovation-Forum/Spectrum-Access-System/blob/master/src/harness/reference_models/propagation/wf_itm_test.py

I am getting around 140 if I give is_height_cbsd_amsl=False and 179 if I give is_height_cbsd_amsl=True.

I feel that 179 is the expected one from Egbert's comments and the test case. However how ITM calculates is not clear, which I try to explain below

Regarding

First remark: the profile graph above does not show visually the Tx and Rx hieght above ground.

Yes, the graph does not show. The Tx (Access Point in this case) is at 300 meters above ground (from AP height in request.json) , and Receiver is around 29.6 meters from ground from ULS data

Here is the graph with that data where the height of the Rx and Tx AGL are added to elevation data

image

Case 1

is_height_cbsd_amsl=True

    accesspoint_ht_m= 300.0 #very high?
    accesspoint_elv = 528.5480346679688

    frequency_assigned=5974.85
    bandwidth_Mhz=30.0
    r_height_to_center_raat=  29.6 
    receiver_elev= 124.12525177001953

    receiver_ht_asml =r_height_to_center_raat +receiver_elev
    accesspoint_ht_asml = accesspoint_ht_m +accesspoint_elv 

    lat1, lng1, height1 = 38.37694444444445, -78.4686111111111, accesspoint_ht_asml #ap
    lat2, lng2, height2 = 37.78341666666667, -78.49219444444445, r_height_to_center_raat #receiver_loc

    reliabilities = 0.2
    #confidence = 0.05  
   res = wf_itm.CalcItmPropagationLoss(lat1, lng1, accesspoint_ht_asml, lat2, lng2, receiver_ht_asml,
                                          reliability=reliabilities, freq_mhz=frequency_assigned,
                                          return_internals=True,is_height_cbsd_amsl=True)

In wf_itm.py

I added the following print statements

  if is_height_cbsd_amsl:
    print(f"height_cbsd={height_cbsd}")
    altitude_cbsd = drive.terrain_driver.GetTerrainElevation(lat_cbsd, lon_cbsd)
    height_cbsd = height_cbsd - altitude_cbsd
    print(f"altitude_cbsd={altitude_cbsd} height_cbsd={height_cbsd} ")

output

height_cbsd=828.5480346679688
altitude_cbsd=534.6803283691254 height_cbsd=293.86770629884336 
height_cbsd=293.86770629884336 height_rx=153.72525177001953
WINNF calculated loss =179.59373347799948 for reliability=0.2

In Case 1, it is basically removing the Elevation height and treating the CBSD height as above ground level. Is this the intention ?

Is it that the library anyway picks up the elevation data and wants only Tx and Rx height AGL ? If so this makes sense

Case 2

Everything else is same but,i s_height_cbsd_amsl=False

res = wf_itm.CalcItmPropagationLoss(lat1, lng1, accesspoint_ht_asml, lat2, lng2, receiver_ht_asml,
                                          reliability=rel, freq_mhz=frequency_assigned,
                                          return_internals=True,is_height_cbsd_amsl=False)
height_cbsd=828.5480346679688 height_rx=153.72525177001953
(WINNF calculated loss =140.94610490716255 for reliability=0.2

This is the root cause in the difference between SAS way of calling ITM and my way.

Is it that we need to just give AGL to ITM like

res = wf_itm.CalcItmPropagationLoss(lat1, lng1, accesspoint_ht_m, lat2, lng2, r_height_to_center_raat,
                                          reliability=reliabilities, freq_mhz=frequency_assigned + bandwidth_Mhz/2,
                                          return_internals=True,is_height_cbsd_amsl=False)

which leads to 

height_cbsd=300.0 height_rx=29.6
WINNF calculated loss =191.42752409644177 for reliability=0.2```

If you take them into account, then you see that the hill is around the direct direction of Tx to Rx, on first approximation:153 + (853-153) * 130 / 370 ~= 390, while the hill goes up to 420m, so not much above

I did not understand this? Are you implying that there is direct line of sight between Rx and Tx (AP) and hence there is not much loss due to the hill.

To test that I removed the hill from the elevation

elevation_data_flt2 = []
for i,v in enumerate(elevation_data_flt):
    if  800 < i < 1200:
        if v > 150:
            v =random.uniform(150,180)
    elevation_data_flt2.append(v)

image

and tested with the same code that was giving around 191path loss. Without the hill this gave a path loss of 140. This means that the hill is obstructing many radio waves, even though there seems to be direct line of sight between the Tx and Rx if we consider the height above ground level for these. I am not a radio expert and @AEgbert can maybe comment here.

itmloss=140.87934958236397

So all in all only one thing to clear regarding ITM lib assuming that everything else is correct,

Is it that we need to just give AGL to ITM for the Tx and Rx points as I mentioned in Case 1 @sergebdt .

sergebdt commented 9 months ago

I am confused, because before you were calling the raw ITM propagation model directly (point_to_point() ). And in your latest message you are using the SAS wrapper which is related to the 3.5GHz SAS system ( wf_itm.CalcItmPropagationLoss). Note that the SAS wrapper does not seem to be the one you need for 6GHz, as the specification for calling the raw ITM are not the same (confidence, reliability values, etc..).

In the point_to_point() low level routine, you have to pass the height of the Tx and Rx as AGL, ie the height above the ground level. That is the way it is treated inside the C routines.

In the SAS tuned ITM, there is the option of passing the height above ground (normal way), or the height above Mean Sea Level (if is_height_cbsd_amsl=True) for various reason pertaining to the SAS requirements. In that case, it corrects the this height by removing the ground altitude, to obtain back the AGL height, so that the low level routine can be called correctly.

So to answer your questions:

In Case 1, it is basically removing the Elevation height and treating the CBSD height as above ground level. Is this the intention ? Is it that the library anyway picks up the elevation data and wants only Tx and Rx height AGL ? If so this makes sense

yes that is correct.

Regarding your case 2, i did not fully understand what is your question.

Note: you are NOT using the same parameters as in case 1. For example in case 1 you use a receiver hieght of 153m AGL, while in case 2 you are using a height of 29m. Remember that the option of considering the hieght as AMSL, only apply for the CBSD part, not the receiver part.

So your case 1 and 2 are clearly not similar.

alexcpn commented 9 months ago

Note that the SAS wrapper does not seem to be the one you need for 6GHz, as the specification for calling the raw ITM are not the same (confidence, reliability values, etc..).

I am not calling the SAS wrapper , I have put that above when I was testing with the SAS test cases. I had modified that to use the 6Ghz settings and even for the point_to_point call in my earlier code

image

In Case 1, it is basically removing the Elevation height and treating the CBSD height as above ground level. Is this the intention ? Is it that the library anyway picks up the elevation data and wants only Tx and Rx height AGL ? If so this makes sense

yes that is correct.

Thanks for the clarification. In the 6GHz we had to use the height ASML for some antenna discrimination calculations and since I was not using the SAS wrapper but directly the itm.point_to_point, I missed out seeing the AGL settings

I will use the AGL and run SIP.2 again and hopefully it should pass. Thanks for your help once again @sergebdt

alexcpn commented 9 months ago

@AEgbert

With the corrected ITM I ran SIP.2 again. Still there is discrepancy; One main is the one in 6660-6670

[INFO] Calculated Frequency
[INFO] 5925.0-5930.025  psd: 23.0 #ok
[INFO] 5930.025-5930.2  psd: 23.0
[INFO] 5930.2-5959.85  psd: 14.063136939772278
[INFO] 5959.85-5989.85  psd: 12.081946244525142
[INFO] 5989.85-6019.5  psd: 12.146334109688098

[INFO] 6019.5-6078.45  psd: 23.0 #not okay [INFO] 5949-6050,upperBound:11.2 nominalValue:9.2

[INFO] 6078.45-6108.45  psd: -5.257910691244206 #ok
[INFO] 6108.45-6137.75  psd: 23.0 #ok
[INFO] 6137.75-6167.75  psd: -5.157789840956426 #ok
[INFO] 6167.75-6167.925  psd: 23.0
[INFO] 6167.925-6182.065  psd: 23.0
[INFO] 6182.065-6211.89  psd: 23.0
[INFO] 6211.89-6241.89  psd: 9.706567876488947

[INFO] 6241.89-6271.54  psd: 13.591725398965394 # not ok -  6241-6302,upperBound:7.1 nominalValue:5.1
[INFO] 6271.54-6300.84  psd: 19.68181382668186

[INFO] 6300.84-6330.84  psd: 13.714437161151011 #ok

[INFO] 6330.84-6375.139999999999  psd: 23.0
[INFO] 6375.139999999999-6380.08  psd: 18.522796420552993
[INFO] 6380.08-6419.965  psd: 23.0
[INFO] 6419.965-6425.0  psd: 23.0
[INFO] 6525.0-6540.0  psd: 23.0
[INFO] 6540.0-6570.0  psd: 22.93042576640596
[INFO] 6570.0-6580.0  psd: 23.0
[INFO] 6580.0-6590.0  psd: 12.380353547182523
[INFO] 6590.0-6630.0  psd: 23.0
[INFO] 6630.0-6640.0  psd: 12.904370326929978
[INFO] 6640.0-6650.0  psd: 23.0
[INFO] 6650.0-6660.0  psd: 0
[INFO] 6660.0-6670.0  psd: -5.059597562215231 # not okay
[INFO] 6670.0-6675.2  psd: 0
[INFO] 6675.2-6680.0  psd: 15.172646904903445
[INFO] 6680.0-6700.0  psd: 23.0
[INFO] 6700.0-6710.0  psd: 10.898452090423874
[INFO] 6710.0-6803.75  psd: 23.0
[INFO] 6803.75-6807.5  psd: 8.01748049112831 #ok
[INFO] 6807.5-6850.0  psd: 23.0
[INFO] 6850.0-6860.0  psd: 5.765331130756209 #ok
[INFO] 6860.0-6875.0  psd: 23.0
[ERROR] gc=131 cn=1,freq_range=(5945.0, 5965.0) expected_eirp={'nominalValue': 22.2, 'upperBound': 24.2} calc_eirp=25.09 diff=0.89
[ERROR] gc=131 cn=5,freq_range=(5965.0, 5985.0) expected_eirp={'nominalValue': 22.2, 'upperBound': 24.2} calc_eirp=25.09 diff=0.89
[ERROR] gc=131 cn=9,freq_range=(5985.0, 6005.0) expected_eirp={'nominalValue': 22.2, 'upperBound': 24.2} calc_eirp=25.09 diff=0.89
[ERROR] gc=131 cn=13,freq_range=(6005.0, 6025.0) expected_eirp={'nominalValue': 22.2, 'upperBound': 24.2} calc_eirp=25.16 diff=0.96
[ERROR] gc=131 cn=17,freq_range=(6025.0, 6045.0) expected_eirp={'nominalValue': 22.2, 'upperBound': 24.2} calc_eirp=36.0 diff=11.80
[ERROR] gc=131 cn=21,freq_range=(6045.0, 6065.0) expected_eirp={'nominalValue': 22.2, 'upperBound': 24.2} calc_eirp=36.0 diff=11.80
[WARNING] gc=131 cn=25,freq_range=(6065.0, 6085.0),expected_eirp={'nominalValue': 10.6, 'upperBound': 12.6} calc_eirp=7.75 diff=4.85
[WARNING] gc=131 cn=29,freq_range=(6085.0, 6105.0),expected_eirp={'nominalValue': 10.6, 'upperBound': 12.6} calc_eirp=7.75 diff=4.85
[WARNING] gc=131 cn=33,freq_range=(6105.0, 6125.0),expected_eirp={'nominalValue': 10.6, 'upperBound': 12.6} calc_eirp=7.75 diff=4.85
[WARNING] gc=131 cn=37,freq_range=(6125.0, 6145.0),expected_eirp={'nominalValue': 10.7, 'upperBound': 12.7} calc_eirp=7.85 diff=4.85
[WARNING] gc=131 cn=41,freq_range=(6145.0, 6165.0),expected_eirp={'nominalValue': 10.7, 'upperBound': 12.7} calc_eirp=7.85 diff=4.85
[WARNING] gc=131 cn=45,freq_range=(6165.0, 6185.0),expected_eirp={'nominalValue': 10.7, 'upperBound': 12.7} calc_eirp=7.85 diff=4.85
[WARNING] gc=131 cn=53,freq_range=(6205.0, 6225.0),expected_eirp={'nominalValue': 25.3, 'upperBound': 27.3} calc_eirp=22.72 diff=4.58
[ERROR] gc=131 cn=57,freq_range=(6225.0, 6245.0) expected_eirp={'nominalValue': 18.1, 'upperBound': 20.1} calc_eirp=22.72 diff=2.62
[ERROR] gc=131 cn=61,freq_range=(6245.0, 6265.0) expected_eirp={'nominalValue': 18.1, 'upperBound': 20.1} calc_eirp=26.6 diff=6.50
[ERROR] gc=131 cn=65,freq_range=(6265.0, 6285.0) expected_eirp={'nominalValue': 18.1, 'upperBound': 20.1} calc_eirp=26.6 diff=6.50
[ERROR] gc=131 cn=69,freq_range=(6285.0, 6305.0) expected_eirp={'nominalValue': 18.1, 'upperBound': 20.1} calc_eirp=26.72 diff=6.62
[ERROR] gc=131 cn=81,freq_range=(6345.0, 6365.0) expected_eirp={'nominalValue': 29.2, 'upperBound': 31.2} calc_eirp=36.0 diff=4.80
[ERROR] gc=131 cn=85,freq_range=(6365.0, 6385.0) expected_eirp={'nominalValue': 29.2, 'upperBound': 31.2} calc_eirp=31.53 diff=0.33
[ERROR] gc=131 cn=89,freq_range=(6385.0, 6405.0) expected_eirp={'nominalValue': 29.3, 'upperBound': 31.3} calc_eirp=36.0 diff=4.70
[ERROR] gc=131 cn=93,freq_range=(6405.0, 6425.0) expected_eirp={'nominalValue': 29.3, 'upperBound': 31.3} calc_eirp=36.0 diff=4.70
[WARNING] gc=131 cn=125,freq_range=(6565.0, 6585.0),expected_eirp={'nominalValue': 27.6, 'upperBound': 29.6} calc_eirp=25.39 diff=4.21
[WARNING] gc=131 cn=129,freq_range=(6585.0, 6605.0),expected_eirp={'nominalValue': 27.6, 'upperBound': 29.6} calc_eirp=25.39 diff=4.21
[ERROR] gc=131 cn=133,freq_range=(6605.0, 6625.0) expected_eirp={'nominalValue': 28.1, 'upperBound': 30.1} calc_eirp=36.0 diff=5.90
[WARNING] gc=131 cn=137,freq_range=(6625.0, 6645.0),expected_eirp={'nominalValue': 28.1, 'upperBound': 30.1} calc_eirp=25.91 diff=4.19
[WARNING] gc=131 cn=149,freq_range=(6685.0, 6705.0),expected_eirp={'nominalValue': 26.4, 'upperBound': 28.4} calc_eirp=23.91 diff=4.49
[WARNING] gc=131 cn=153,freq_range=(6705.0, 6725.0),expected_eirp={'nominalValue': 26.4, 'upperBound': 28.4} calc_eirp=23.91 diff=4.49
[ERROR] gc=131 cn=165,freq_range=(6765.0, 6785.0) expected_eirp={'nominalValue': 31.0, 'upperBound': 33.0} calc_eirp=36.0 diff=3.00
[WARNING] gc=131 cn=169,freq_range=(6785.0, 6805.0),expected_eirp={'nominalValue': 23.8, 'upperBound': 25.8} calc_eirp=21.03 diff=4.77
[WARNING] gc=131 cn=173,freq_range=(6805.0, 6825.0),expected_eirp={'nominalValue': 23.8, 'upperBound': 25.8} calc_eirp=21.03 diff=4.77
[ERROR] gc=132 cn=3,freq_range=(5945.0, 5985.0) expected_eirp={'nominalValue': 25.2, 'upperBound': 27.2} calc_eirp=28.1 diff=0.90
[ERROR] gc=132 cn=11,freq_range=(5985.0, 6025.0) expected_eirp={'nominalValue': 25.2, 'upperBound': 27.2} calc_eirp=28.1 diff=0.90
[ERROR] gc=132 cn=19,freq_range=(6025.0, 6065.0) expected_eirp={'nominalValue': 25.2, 'upperBound': 27.2} calc_eirp=32.07 diff=4.87
[WARNING] gc=132 cn=27,freq_range=(6065.0, 6105.0),expected_eirp={'nominalValue': 13.6, 'upperBound': 15.6} calc_eirp=10.76 diff=4.84
[WARNING] gc=132 cn=35,freq_range=(6105.0, 6145.0),expected_eirp={'nominalValue': 13.6, 'upperBound': 15.6} calc_eirp=10.76 diff=4.84
[WARNING] gc=132 cn=43,freq_range=(6145.0, 6185.0),expected_eirp={'nominalValue': 13.7, 'upperBound': 15.7} calc_eirp=10.86 diff=4.84
[WARNING] gc=132 cn=51,freq_range=(6185.0, 6225.0),expected_eirp={'nominalValue': 28.3, 'upperBound': 30.3} calc_eirp=25.73 diff=4.57
[ERROR] gc=132 cn=59,freq_range=(6225.0, 6265.0) expected_eirp={'nominalValue': 21.1, 'upperBound': 23.1} calc_eirp=25.73 diff=2.63
[ERROR] gc=132 cn=67,freq_range=(6265.0, 6305.0) expected_eirp={'nominalValue': 21.1, 'upperBound': 23.1} calc_eirp=29.61 diff=6.51
[ERROR] gc=132 cn=83,freq_range=(6345.0, 6385.0) expected_eirp={'nominalValue': 32.3, 'upperBound': 34.3} calc_eirp=34.54 diff=0.24
[ERROR] gc=132 cn=91,freq_range=(6385.0, 6425.0) expected_eirp={'nominalValue': 32.3, 'upperBound': 34.3} calc_eirp=36.0 diff=1.70
[WARNING] gc=132 cn=123,freq_range=(6545.0, 6585.0),expected_eirp={'nominalValue': 30.6, 'upperBound': 32.6} calc_eirp=28.4 diff=4.20
[WARNING] gc=132 cn=131,freq_range=(6585.0, 6625.0),expected_eirp={'nominalValue': 30.6, 'upperBound': 32.6} calc_eirp=28.4 diff=4.20
[WARNING] gc=132 cn=155,freq_range=(6705.0, 6745.0),expected_eirp={'nominalValue': 29.4, 'upperBound': 31.4} calc_eirp=26.92 diff=4.48
[WARNING] gc=132 cn=171,freq_range=(6785.0, 6825.0),expected_eirp={'nominalValue': 26.8, 'upperBound': 28.8} calc_eirp=24.04 diff=4.76
[ERROR] gc=133 cn=7,freq_range=(5945.0, 6025.0) expected_eirp={'nominalValue': 28.2, 'upperBound': 30.2} calc_eirp=31.11 diff=0.91
[WARNING] gc=133 cn=23,freq_range=(6025.0, 6105.0),expected_eirp={'nominalValue': 16.6, 'upperBound': 18.6} calc_eirp=13.77 diff=4.83
[WARNING] gc=133 cn=39,freq_range=(6105.0, 6185.0),expected_eirp={'nominalValue': 16.6, 'upperBound': 18.6} calc_eirp=13.77 diff=4.83
[ERROR] gc=133 cn=55,freq_range=(6185.0, 6265.0) expected_eirp={'nominalValue': 24.1, 'upperBound': 26.1} calc_eirp=28.74 diff=2.64
[ERROR] gc=133 cn=71,freq_range=(6265.0, 6345.0) expected_eirp={'nominalValue': 24.1, 'upperBound': 26.1} calc_eirp=32.62 diff=6.52
[WARNING] gc=133 cn=167,freq_range=(6745.0, 6825.0),expected_eirp={'nominalValue': 29.8, 'upperBound': 31.8} calc_eirp=27.05 diff=4.75
[WARNING] gc=134 cn=15,freq_range=(5945.0, 6105.0),expected_eirp={'nominalValue': 19.6, 'upperBound': 21.6} calc_eirp=16.78 diff=4.82
[WARNING] gc=134 cn=47,freq_range=(6105.0, 6265.0),expected_eirp={'nominalValue': 19.7, 'upperBound': 21.7} calc_eirp=16.78 diff=4.92
[ERROR] gc=134 cn=79,freq_range=(6265.0, 6425.0) expected_eirp={'nominalValue': 27.2, 'upperBound': 29.2} calc_eirp=35.63 diff=6.43

Detailed for this

[INFO] ********************************************************************************
[INFO] Going to process UUID = 964659 call_sign = 'KRQ50
[INFO] accesspoint_loc =(38.377266, -78.468021) receiver_loc=(37.50347222222222, -77.54163888888888)
[INFO] Processing UUID = 964659 call_sign = 'KRQ50' R to AP Dist =126638.39532923009 m, CF =6665.0 Mhz r_antenna_make= None r_antenna_model=None
[INFO] uuid: 964659
[INFO] call_sign: KRQ50
[INFO] frequency_assigned: 6665.0
[INFO] receiver_loc: (37.50347222222222, -77.54163888888888)
[INFO] t_loc_num: 1
[INFO] t_path_num: 1
[INFO] t_ant_num: 1
[INFO] receiver_to_ap_dist_m: 126638.39532923009
[INFO] r_height_to_center_raat: 97.5
[INFO] r_gain: 43.9
[INFO] r_diversity_gain: 42.3
[INFO] r_azimuth: None
[INFO] r_antenna_make: None
[INFO] r_antenna_model: None
[INFO] r_loc_num: 2
[INFO] r_path_num: 1
[INFO] r_ant_num: 1
[INFO] polarization_code: None
[INFO] line_loss: None
[INFO] r_diameter_ft: 6
[INFO] r_diameter_m: 1.83
[INFO] ant_category: B1
[INFO] transmitter_loc: (37.80763888888889, -77.86413888888889)
[INFO] t_height_to_center_raat: 106.7
[INFO] t_gain: 43.9
[INFO] t_diversity_gain: 42.3
[INFO] t_azimuth: 139.8
[INFO] t_antenna_make: None
[INFO] t_antenna_model: None
[INFO] bandwidth: 10.0
[INFO] frequency_range: (6660.0, 6670.0)
[INFO] accesspoint_loc: (38.377266, -78.468021)
[INFO] accesspoint_ht_m: 300.0
[INFO] accesspoint_vtunc_m: 30
[INFO] Free Space Path Loss =150.97559684080932
[INFO] Total distance of segments =0.00 km Total profiles= 1
[INFO] Total distance of segments =0.00 km Total profiles= 1
[INFO] Total distance of segments =0.00 km Total profiles= 1
[INFO] accesspoint_elev=528.5480346679688 receiver_elev=90.7787094116211 transmitter_elev=103.4180679321289
[INFO] GRx = 43.9 dB
[INFO] centerFreq of Receiver =6665.0 Mhz
[INFO] ant_category = B1 
[INFO] Angle 1: Azimuthal discrimination angle:R-AP The azimuthal discrimination angle,  θAZ_Disc= 0.2966525206032884 degress
[INFO] Angle 2: Elevation angle: R-T The receive antenna boresight elevation angle (from transmmitter) θEL_Disc= -0.12047307252150419 degrees
[INFO] Angle 3:elevation angle: R-AP The elevation angle from the  receive antenna to the evaluation phi= -0.1371332383835061 degrees
[INFO] --------------------------------------------------------------------------------
[INFO] Overall discrimination angle, 𝛾 =0.29711922480455477 degress
[INFO] --------------------------------------------------------------------------------
[INFO] 2.a GRx=43.9 >= 38:Calcualted:rpe=43.53519440302455 rpe-GRx=-0.3648055969754509
[INFO] getRPE gamma=0.2966525206032884 ant_category=B1 rpe=43.53519440302455 
[INFO] The azimuthal discrimination= GAZ-Disc =-0.3648055969754509 This should be negative
[INFO] The elevation discrimination  GEL-Disc in dB = -0.3648055969754509 contribution This should be Negative!)
[INFO] --------------------------------------------------------------------------------
[INFO] GRx_Effectivein dB = 40.53519440302455 [43.9 -0.3636583770246921] [-0.0011472199507587522- polarization mismatch=3]
[INFO] ----GRx_Effective in dB = 40.53519440302455
[INFO] Near field adjustments do not have to be done
[INFO] Total distance of segments =127.59 km Total profiles= 1512
[INFO] Elevation Profile and ITM losss caluclated  in 0.002701 seconds
[INFO] ITM loss is 147.6408938847586
[INFO] Path Loss used =150.97559684080932
[INFO] building_entry_loss = 0 ap_indoor=False
[INFO] interference = -110.44040243778477 
[INFO] transmit_power PSD = -5.059597562215231 dBm/MHz
[INFO] ********************************************************************************

To cross check the path loss, I ran this directly in SAS test case (with 6GHz ITM setting to cross check);

The path loss is coming here the same as mine and pretty low around 140, though this also has a hill in between and is similar to the one in an above comment (5974.85 MHz) which is giving about 191 path loss by the same SAS code (with 6GHz specific ITM setting) as well as mine now)

image

def test_op_mode2(self):

    # Alex modified test case
    # note if you get OSError: NED Tile (38,-78) not found.
    # alex@pop-os:/data/Common-Data/data/ned$ ls | grep 38 | grep 78
    # usgs_ned_1_n38w078_gridfloat_std.zip and unzip this and copy to src/harness/reference_models/geo/testdata/ned/
    # /data/Common-Data/data/ned$ unzip usgs_ned_1_n38w078_gridfloat_std.zip -d /home/alex/coding/Spectrum-Access-System/src/harness/reference_models/geo/testdata/ned/

    accesspoint_loc =(38.377266, -78.468021)
    receiver_loc=(37.50347222222222, -77.54163888888888)
    frequency_assigned=6665
    bandwidth_Mhz=10.0
    accesspoint_ht_m = 300 #m
    r_height_to_center_raat = 97.5
    frequency =frequency_assigned + bandwidth_Mhz/2

    lat1, lng1  = accesspoint_loc  #ap
    lat2, lng2  = receiver_loc  #receiver_loc

    reliabilities = 0.2
    #confidence = 0.05  
    res = wf_itm.CalcItmPropagationLoss(lat1, lng1, accesspoint_ht_m, lat2, lng2, r_height_to_center_raat,
                                          reliability=reliabilities, freq_mhz=frequency_assigned + bandwidth_Mhz/2,
                                          return_internals=True,is_height_cbsd_amsl=False)
    print(f"WINNF calculated loss ={res.db_loss} for reliability={reliabilities}")

Output

alex@pop-os:~/coding/Spectrum-Access-System/src/harness/reference_models/propagation$ python3 wf_itm_test.py
ITU_TEST_DIR /home/alex/coding/Spectrum-Access-System/src/harness/reference_models/propagation/../geo/testdata/itu
TERRAIN_TEST_DIR /home/alex/coding/Spectrum-Access-System/src/harness/reference_models/propagation/../geo/testdata/ned
**  tile_name is usgs_ned_1_n38w079_gridfloat_std.flt
**  tile_name is usgs_ned_1_n38w078_gridfloat_std.flt
**  tile_name is usgs_ned_1_n39w079_gridfloat_std.flt
its_elev Max points 1496
height_cbsd=300 height_rx=97.5
dielec, conductivity, refractivity, freq_mhz,climate, polarization, confidence, reliabilities, mdvar
25.0 0.02 314 6670.0 5 0 0.05 0.2 13
loss=147.43723282541362
WINNF calculated loss =147.43723282541362 for reliability=0.2
sergebdt commented 9 months ago

So if I understand well, you are saying: 1) calling the SAS code that does everything (collecting data with 1500 euqlly spaced points and computation) versus your own internal implementation provide the same result (more or less). 2) this result is around 140dB path loss -- which is the expected free space path loss ==> this might be logical since your hill seems to be lower than the direct line of sight 3) your implementation is however failing the test sometimes

Is this correct ?

Unfortunately I cannot be of great help as I dont know what these tests are doing exactly, nor your implementation, nor the reference implementation used for defining the tests.

As I said earlier, the ITM code has non linearities, and it is easy to see large change of pathloss when you hit a condition that triggers a change of regime. Main regimes are obtained from the str_mode and err_num code, although these are not exhaustive in determining if you hit a non linearity ==> you may want to print these codes to check if change of regime occur. Perhaps, in your example above the hill may trigger such non linearity since it is quite close for the direct path of view.

In conclusion, it may be sufficient for your implementation to have slight difference with the reference implementation for triggering such type of failure. Is there a reference implementation that passes the tests, from which you can compare exactly where your implementation diverge ?

In SAS, for the purpose to avoid such issues, the exact reference implementation for computing pathloss is provided in the GitHub, and all implementations from various companies follows this very closely to avoid such failure:

alexcpn commented 9 months ago

calling the SAS code that does everything (collecting data with 1500 euqlly spaced points and computation) versus your own internal implementation provide the same result (more or less). this result is around 140dB path loss -- which is the expected free space path loss ==> this might be logical since your hill seems to be lower than the direct line of sight your implementation is however failing the test sometimes Is this correct ?

Yes.

. Is there a reference implementation that passes the tests, from which you can compare exactly where your implementation diverges?

No, at least not published or mentioned in any of the docs. Mainly there are very detailed specifications from WINNF which are in https://6ghz.wirelessinnovation.org/baseline-standards. (most important being WINNF-TS-1014 v1.6) derived out from the FCC master specification Unlicensed Use of the 6 GHz Band https://docs.fcc.gov/public/attachments/DOC-363490A1.pdf

Further in this repo, there are data provided of ULS snapshots and NED files. As well as test requests, and response

All these are great resources and are very helpful.

But there is no Github repo like for SAS with a reference implementation of any of the path loss that we need to follow or for the calculation for the effective Rx gain, that an AFC provider can check with or use as a base

@AEgbert or others in this repo can comment if anything like that is planned

For the ITM loss- it would be good if the SAS repo was forked and a simple method added with 6GHz parameters for ITM loss and give a reference to this in WINNF-TS-1014v1.6 document if the test vectors were indeed derived out of the SAS ITM implementation

AEgbert commented 9 months ago

But there is no Github repo like for SAS with a reference implementation of any of the path loss that we need to follow or for the calculation for the effective Rx gain, that an AFC provider can check with or use as a base @AEgbert or others in this repo can comment if anything like that is planned

To my knowledge, no reference AFC implementation is planned at this time.

alexcpn commented 9 months ago

thanks @AEgbert ; the main question related to this issue is this outstanding frequency which is quite a lot of mismatch from the expected frequency . Could you kindly comment on this

With the corrected ITM I ran SIP.2 again. Still there is discrepancy; One main is the one in 6660-6670

[INFO] Calculated Frequency [INFO] 5925.0-5930.025 psd: 23.0 #ok [INFO] 5930.025-5930.2 psd: 23.0 [INFO] 5930.2-5959.85 psd: 14.063136939772278 [INFO] 5959.85-5989.85 psd: 12.081946244525142 [INFO] 5989.85-6019.5 psd: 12.146334109688098 [INFO] 6019.5-6078.45 psd: 23.0 #not okay [INFO] 5949-6050,upperBound:11.2 nominalValue:9.2 [INFO] 6078.45-6108.45 psd: -5.257910691244206 #ok [INFO] 6108.45-6137.75 psd: 23.0 #ok [INFO] 6137.75-6167.75 psd: -5.157789840956426 #ok [INFO] 6167.75-6167.925 psd: 23.0 [INFO] 6167.925-6182.065 psd: 23.0 [INFO] 6182.065-6211.89 psd: 23.0 [INFO] 6211.89-6241.89 psd: 9.706567876488947 [INFO] 6241.89-6271.54 psd: 13.591725398965394 # not ok - 6241-6302,upperBound:7.1 nominalValue:5.1 [INFO] 6271.54-6300.84 psd: 19.68181382668186 [INFO] 6300.84-6330.84 psd: 13.714437161151011 #ok [INFO] 6330.84-6375.139999999999 psd: 23.0 [INFO] 6375.139999999999-6380.08 psd: 18.522796420552993 [INFO] 6380.08-6419.965 psd: 23.0 [INFO] 6419.965-6425.0 psd: 23.0 [INFO] 6525.0-6540.0 psd: 23.0 [INFO] 6540.0-6570.0 psd: 22.93042576640596 [INFO] 6570.0-6580.0 psd: 23.0 [INFO] 6580.0-6590.0 psd: 12.380353547182523 [INFO] 6590.0-6630.0 psd: 23.0 [INFO] 6630.0-6640.0 psd: 12.904370326929978 [INFO] 6640.0-6650.0 psd: 23.0 [INFO] 6650.0-6660.0 psd: 0 [INFO] 6660.0-6670.0 psd: -5.059597562215231 # not okay [INFO] 6670.0-6675.2 psd: 0 [INFO] 6675.2-6680.0 psd: 15.172646904903445 [INFO] 6680.0-6700.0 psd: 23.0 [INFO] 6700.0-6710.0 psd: 10.898452090423874 [INFO] 6710.0-6803.75 psd: 23.0 [INFO] 6803.75-6807.5 psd: 8.01748049112831 #ok [INFO] 6807.5-6850.0 psd: 23.0 [INFO] 6850.0-6860.0 psd: 5.765331130756209 #ok [INFO] 6860.0-6875.0 psd: 23.0

More details regarding this. 

[INFO] ********************************************************************************
[INFO] Going to process UUID = 964659 call_sign = 'KRQ50
[INFO] accesspoint_loc =(38.377266, -78.468021) receiver_loc=(37.50347222222222, -77.54163888888888)
[INFO] Processing UUID = 964659 call_sign = 'KRQ50' R to AP Dist =126638.39532923009 m, CF =6665.0 Mhz r_antenna_make= None r_antenna_model=None
[INFO] uuid: 964659
[INFO] call_sign: KRQ50
[INFO] frequency_assigned: 6665.0
[INFO] receiver_loc: (37.50347222222222, -77.54163888888888)
[INFO] t_loc_num: 1
[INFO] t_path_num: 1
[INFO] t_ant_num: 1
[INFO] receiver_to_ap_dist_m: 126638.39532923009
[INFO] r_height_to_center_raat: 97.5
[INFO] r_gain: 43.9
[INFO] r_diversity_gain: 42.3
[INFO] r_azimuth: None
[INFO] r_antenna_make: None
[INFO] r_antenna_model: None
[INFO] r_loc_num: 2
[INFO] r_path_num: 1
[INFO] r_ant_num: 1
[INFO] polarization_code: None
[INFO] line_loss: None
[INFO] r_diameter_ft: 6
[INFO] r_diameter_m: 1.83
[INFO] ant_category: B1
[INFO] transmitter_loc: (37.80763888888889, -77.86413888888889)
[INFO] t_height_to_center_raat: 106.7
[INFO] t_gain: 43.9
[INFO] t_diversity_gain: 42.3
[INFO] t_azimuth: 139.8
[INFO] t_antenna_make: None
[INFO] t_antenna_model: None
[INFO] bandwidth: 10.0
[INFO] frequency_range: (6660.0, 6670.0)
[INFO] accesspoint_loc: (38.377266, -78.468021)
[INFO] accesspoint_ht_m: 300.0
[INFO] accesspoint_vtunc_m: 30
[INFO] accesspoint_hzunc_x: 150
[INFO] accesspoint_hzunc_y: 150
[INFO] Free Space Path Loss =150.97559684080932
[INFO] Total distance of segments =0.00 km Total profiles= 1
[INFO] Total distance of segments =0.00 km Total profiles= 1
[INFO] receiver_elev=90.125 transmitter_elev=104.3125
[INFO] Total distance of segments =0.00 km Total profiles= 1
[INFO] accesspoint_elev=527.0
[INFO] GRx = 43.9 dB
[INFO] centerFreq of Receiver =6665.0 Mhz
[INFO] ant_category = B1 
[INFO] Angle 1: Azimuthal discrimination angle:R-AP The azimuthal discrimination angle,  θAZ_Disc= 0.2966525206032884 degress
[INFO] Angle 2: Elevation angle: R-T The receive antenna boresight elevation angle (from transmmitter) θEL_Disc= -0.11846414468559771 degrees
[INFO] Angle 3:elevation angle: R-AP The elevation angle from the  receive antenna to the evaluation phi= -0.1375378610744928 degrees
[INFO] --------------------------------------------------------------------------------
[INFO] Overall discrimination angle, 𝛾 =0.2972643345740801 degress
[INFO] --------------------------------------------------------------------------------
[INFO] 2.a GRx=43.9 >= 38:Calcualted:rpe=43.53483798190758 rpe-GRx=-0.36516201809241977
[INFO] getRPE gamma=0.2966525206032884 ant_category=B1 rpe=43.53483798190758 
[INFO] The azimuthal discrimination= GAZ-Disc =-0.36516201809241977 This should be negative
[INFO] The elevation discrimination  GEL-Disc in dB = -0.36516201809241977 contribution This should be Negative!)
[INFO] --------------------------------------------------------------------------------
[INFO] GRx_Effectivein dB = 40.53483798190758 [43.9 -0.36365836790411493] [-0.0015036501883048208- polarization mismatch=3]
[INFO] ----GRx_Effective in dB = 40.53483798190758
[INFO] Near field adjustments do not have to be done
[INFO] Total distance of segments =126.65 km Total profiles= 1501
[INFO] ITM loss is 147.6242177505957
[INFO] Path Loss used =150.97559684080932
[INFO] building_entry_loss = 0 ap_indoor=False
[INFO] interference = -110.44075885890175 
[INFO] transmit_power PSD = -5.059241141098255 dBm/MHz
[INFO] ********************************************************************************
AEgbert commented 9 months ago

Still there is discrepancy; One main is the one in 6660-6670

I believe this is due to the Very Large Array (NM), which is protected on the band 6660-6675.2 per R0-AIP-05. As such, this band is excluded from the test vector mask as no transmission is allowed.

alexcpn commented 9 months ago

Thanks @AEgbert . The way I was post-processing/splitting frequency had a bug were I missed accounting for the frequency in between. 

[INFO] 6640.0-6650.0  psd: 21.4265802717606
[INFO] 6650.0-6660.0  psd: 0
[INFO] 6660.0-6670.0  psd: -3.4592411410982606 --> should have been set to zero
[INFO] 6670.0-6675.2  psd: 0
[INFO] 6675.2-6680.0  psd: 15.173694586351644
[INFO] 6680.0-6700.0  psd: 23.0
[INFO] 6700.0-6710.0  psd: 10.900147473341178

Correcting for that gives good results now; Except for one frequency range 6610-6640 which I seem to be missing. I will run this again with more details and cross-check on Monday

[INFO] Calculated Frequency
[INFO] 5925.0-5930.025  psd: 23.0 - ok
[INFO] 5930.025-5990.025  psd: 9.262802906932748 -ok
[INFO] 5990.025-6049.325  psd: 9.34836855972027 -ok
[INFO] 6049.325-6078.45  psd: 21.774263759910923 -ok
[INFO] 6078.45-6108.45  psd: -3.8572220400994297 -ok
[INFO] 6108.45-6137.75  psd: 23.0 -ok
[INFO] 6137.75-6167.75  psd: -3.7570877210145284 -ok
[INFO] 6167.75-6167.925  psd: 23.0
[INFO] 6167.925-6182.065  psd: 23.0
[INFO] 6182.065-6211.89  psd: 23.0
[INFO] 6211.89-6241.365  psd: 9.712074510285504 ok
[INFO] 6241.365-6241.54  psd: 5.428988179510682
[INFO] 6241.54-6271.54  psd: 5.3877793825977705
[INFO] 6271.54-6301.19  psd: 5.428746953713727
[INFO] 6301.19-6301.365  psd: 5.428988179510682 -ok
[INFO] 6301.365-6330.84  psd: 13.71456043313799 -ok
[INFO] 6330.84-6360.14  psd: 23.0
[INFO] 6360.14-6390.14  psd: 18.79249186610977
[INFO] 6390.14-6419.965  psd: 23.0
[INFO] 6419.965-6425.0  psd: 23.0
[INFO] 6525.0-6540.0  psd: 23.0
[INFO] 6540.0-6570.0  psd: 23.0
[INFO] 6570.0-6580.0  psd: 23.0
[INFO] 6580.0-6590.0  psd: 12.38092174826265
[INFO] 6590.0-6600.0  psd: 22.25159676698064
[INFO] 6600.0-6630.0  psd: 23.0
[INFO] 6630.0-6640.0  psd: 12.903790669442799 # not ok 6610-6640,upperBound:17.1 nominalValue:15.1
[INFO] 6640.0-6650.0  psd: 23.0
[INFO] 6650.0-6675.2  psd: 0
[INFO] 6675.2-6680.0  psd: 15.173694586351644
[INFO] 6680.0-6700.0  psd: 23.0
[INFO] 6700.0-6710.0  psd: 10.900147473341178
[INFO] 6710.0-6803.75  psd: 23.0
[INFO] 6803.75-6807.5  psd: 8.015649131148109
[INFO] 6807.5-6850.0  psd: 23.0
[INFO] 6850.0-6860.0  psd: 5.767857281582451 -ok
[INFO] 6860.0-6870.0  psd: 12.108471251649718
[INFO] 6870.0-6875.0  psd: 23.0
[WARNING] gc=131 cn=53,freq_range=(6205.0, 6225.0),expected_eirp={'nominalValue': 25.3, 'upperBound': 27.3} calc_eirp=22.72 diff=4.58 #okish
[ERROR] gc=131 cn=81,freq_range=(6345.0, 6365.0) expected_eirp={'nominalValue': 29.2, 'upperBound': 31.2} calc_eirp=31.8 diff=0.60 #slightly high
[ERROR] gc=131 cn=85,freq_range=(6365.0, 6385.0) expected_eirp={'nominalValue': 29.2, 'upperBound': 31.2} calc_eirp=31.8 diff=0.60 #slightly high
[ERROR] gc=131 cn=89,freq_range=(6385.0, 6405.0) expected_eirp={'nominalValue': 29.3, 'upperBound': 31.3} calc_eirp=31.8 diff=0.50#slightly high
[ERROR] gc=131 cn=93,freq_range=(6405.0, 6425.0) expected_eirp={'nominalValue': 29.3, 'upperBound': 31.3} calc_eirp=36.0 diff=4.70#slightly high
[WARNING] gc=131 cn=125,freq_range=(6565.0, 6585.0),expected_eirp={'nominalValue': 27.6, 'upperBound': 29.6} calc_eirp=25.39 diff=4.21 
[WARNING] gc=131 cn=129,freq_range=(6585.0, 6605.0),expected_eirp={'nominalValue': 27.6, 'upperBound': 29.6} calc_eirp=25.39 diff=4.21
[ERROR] gc=131 cn=133,freq_range=(6605.0, 6625.0) expected_eirp={'nominalValue': 28.1, 'upperBound': 30.1} calc_eirp=36.0 diff=5.90 #problem
[WARNING] gc=131 cn=137,freq_range=(6625.0, 6645.0),expected_eirp={'nominalValue': 28.1, 'upperBound': 30.1} calc_eirp=25.91 diff=4.19
[WARNING] gc=131 cn=149,freq_range=(6685.0, 6705.0),expected_eirp={'nominalValue': 26.4, 'upperBound': 28.4} calc_eirp=23.91 diff=4.49
[WARNING] gc=131 cn=153,freq_range=(6705.0, 6725.0),expected_eirp={'nominalValue': 26.4, 'upperBound': 28.4} calc_eirp=23.91 diff=4.49
[ERROR] gc=131 cn=165,freq_range=(6765.0, 6785.0) expected_eirp={'nominalValue': 31.0, 'upperBound': 33.0} calc_eirp=36.0 diff=3.00
[WARNING] gc=131 cn=169,freq_range=(6785.0, 6805.0),expected_eirp={'nominalValue': 23.8, 'upperBound': 25.8} calc_eirp=21.03 diff=4.77
[WARNING] gc=131 cn=173,freq_range=(6805.0, 6825.0),expected_eirp={'nominalValue': 23.8, 'upperBound': 25.8} calc_eirp=21.03 diff=4.77
[WARNING] gc=132 cn=51,freq_range=(6185.0, 6225.0),expected_eirp={'nominalValue': 28.3, 'upperBound': 30.3} calc_eirp=25.73 diff=4.57
[ERROR] gc=132 cn=83,freq_range=(6345.0, 6385.0) expected_eirp={'nominalValue': 32.3, 'upperBound': 34.3} calc_eirp=34.81 diff=0.51
[ERROR] gc=132 cn=91,freq_range=(6385.0, 6425.0) expected_eirp={'nominalValue': 32.3, 'upperBound': 34.3} calc_eirp=34.81 diff=0.51
[WARNING] gc=132 cn=123,freq_range=(6545.0, 6585.0),expected_eirp={'nominalValue': 30.6, 'upperBound': 32.6} calc_eirp=28.4 diff=4.20
[WARNING] gc=132 cn=131,freq_range=(6585.0, 6625.0),expected_eirp={'nominalValue': 30.6, 'upperBound': 32.6} calc_eirp=28.4 diff=4.20
[WARNING] gc=132 cn=155,freq_range=(6705.0, 6745.0),expected_eirp={'nominalValue': 29.4, 'upperBound': 31.4} calc_eirp=26.92 diff=4.48
[WARNING] gc=132 cn=171,freq_range=(6785.0, 6825.0),expected_eirp={'nominalValue': 26.8, 'upperBound': 28.8} calc_eirp=24.04 diff=4.76
[WARNING] gc=133 cn=167,freq_range=(6745.0, 6825.0),expected_eirp={'nominalValue': 29.8, 'upperBound': 31.8} calc_eirp=27.05 diff=4.75
[INFO] Time in Milliseconds 0:00:24.744613, processed_count=1941
alexcpn commented 9 months ago

Updating with the increased uncertainty region as in #51 I am getting matching results except for the last band where my calculations are 10 dB lower.

However the bigger problem is that with this uncertainty region, the code have to process about 30 million entries and it is taking about 2 hours

SIP.2

[INFO] Length of filtered cords within 150000 is 4410

[INFO] Expected Frequency
[INFO] 5925-5930,upperBound:23.0 nominalValue:23.0
[INFO] 5930-5949,upperBound:11.1 nominalValue:9.1
[INFO] 5949-6050,upperBound:11.2 nominalValue:9.2
[INFO] 6050-6078,upperBound:23.0 nominalValue:23.0
[INFO] 6078-6109,upperBound:-0.4 nominalValue:-2.4
[INFO] 6109-6137,upperBound:23.0 nominalValue:23.0
[INFO] 6137-6168,upperBound:-0.3 nominalValue:-2.3
[INFO] 6168-6211,upperBound:23.0 nominalValue:23.0
[INFO] 6211-6241,upperBound:14.3 nominalValue:12.3
[INFO] 6241-6302,upperBound:7.1 nominalValue:5.1
[INFO] 6302-6331,upperBound:17.2 nominalValue:15.2
[INFO] 6331-6359,upperBound:20.9 nominalValue:18.9
[INFO] 6359-6392,upperBound:18.2 nominalValue:16.2
[INFO] 6392-6420,upperBound:18.3 nominalValue:16.3
[INFO] 6420-6425,upperBound:23.0 nominalValue:23.0
[INFO] 6525-6540,upperBound:23.0 nominalValue:23.0
[INFO] 6540-6570,upperBound:23.0 nominalValue:22.9
[INFO] 6570-6580,upperBound:23.0 nominalValue:23.0
[INFO] 6580-6590,upperBound:16.6 nominalValue:14.6
[INFO] 6590-6600,upperBound:23.0 nominalValue:22.7
[INFO] 6600-6610,upperBound:23.0 nominalValue:23.0
[INFO] 6610-6640,upperBound:17.1 nominalValue:15.1
[INFO] 6640-6650,upperBound:23.0 nominalValue:23.0
[INFO] 6676-6680,upperBound:19.9 nominalValue:17.9
[INFO] 6680-6694,upperBound:23.0 nominalValue:21.3
[INFO] 6694-6700,upperBound:23.0 nominalValue:21.4
[INFO] 6700-6710,upperBound:15.4 nominalValue:13.4
[INFO] 6710-6770,upperBound:23.0 nominalValue:23.0
[INFO] 6770-6800,upperBound:20.0 nominalValue:18.0
[INFO] 6800-6803,upperBound:23.0 nominalValue:23.0
[INFO] 6803-6808,upperBound:12.8 nominalValue:10.8
[INFO] 6808-6810,upperBound:23.0 nominalValue:23.0
[INFO] 6810-6820,upperBound:23.0 nominalValue:21.0
[INFO] 6820-6850,upperBound:23.0 nominalValue:23.0
[INFO] 6850-6860,upperBound:8.8 nominalValue:6.8
[INFO] 6860-6870,upperBound:17.1 nominalValue:15.1
[INFO] 6870-6875,upperBound:23.0 nominalValue:23.0
[INFO] Calculated Frequency
[INFO] 5925.0-5930.025  psd: 23.0 #ok
[INFO] 5930.025-5990.025  psd: 9.162855109773304 #ok
[INFO] 5990.025-6049.325  psd: 9.248849335298857 #ok
[INFO] 6049.325-6078.45  psd: 21.725732881676123 #ok
[INFO] 6078.45-6108.45  psd: -3.893601986183313 #ok
[INFO] 6108.45-6137.75  psd: 23.0 #ok
[INFO] 6137.75-6167.75  psd: -3.7932845988204917 #ok
[INFO] 6167.75-6167.925  psd: 23.0 #ok
[INFO] 6167.925-6182.065  psd: 23.0 #ok
[INFO] 6182.065-6211.89  psd: 23.0#ok
[INFO] 6211.89-6241.365  psd: 9.674139423097031 #ok
[INFO] 6241.365-6241.54  psd: 5.107086117782345 #ok
[INFO] 6241.54-6271.54  psd: 5.0865290694984395 #ok
[INFO] 6271.54-6301.365  psd: 5.107086117782345 #ok
[INFO] 6301.365-6330.84  psd: 12.130862002759528 #ok
[INFO] 6330.84-6359.965  psd: 15.87044751566225 #ok
[INFO] 6359.965-6419.965  psd: 13.239783921163593 #ok
[INFO] 6419.965-6425.0  psd: 23.0 #ok
[INFO] 6525.0-6540.0  psd: 23.0 #ok
[INFO] 6540.0-6570.0  psd: 20.05168643652047 #ok
[INFO] 6570.0-6580.0  psd: 23.0 #ok
[INFO] 6580.0-6590.0  psd: 12.34298884470428 #ok
[INFO] 6590.0-6600.0  psd: 22.184274075964964 #ok
[INFO] 6600.0-6610.0  psd: 20.232672164996245 #ok
[INFO] 6610.0-6640.0  psd: 12.549264481357142 #ok
[INFO] 6640.0-6650.0  psd: 23.0 #ok
[INFO] 6650.0-6675.2  psd: 0 #ok
[INFO] 6675.2-6680.0  psd: 15.143535220560409 #ok
[INFO] 6680.0-6700.0  psd: 20.139941542990556 #ok
[INFO] 6700.0-6710.0  psd: 10.854650405008215 #ok
[INFO] 6710.0-6770.0  psd: 23.0 #ok
[INFO] 6770.0-6800.0  psd: 14.952141274939152 #ok
[INFO] 6800.0-6803.75  psd: 23.0 #ok
[INFO] 6803.75-6807.5  psd: 7.986306331845867 #ok
[INFO] 6807.5-6810.0  psd: 23.0 #ok
[INFO] 6810.0-6820.0  psd: 20.964231584415444 #ok
[INFO] 6820.0-6830.0  psd: 23.0 #ok
[INFO] 6830.0-6850.0  psd: 20.277231620836176 #ok
[INFO] 6850.0-6860.0  psd: 5.7319583598293065 #ok
[INFO] 6860.0-6870.0  psd: 12.080383470008258 #ok
[INFO] 6870.0-6875.0  psd: 13.910187221438406 #ok (but too low)
[WARNING] gc=131 cn=53,freq_range=(6205.0, 6225.0),expected_eirp={'nominalValue': 25.3, 'upperBound': 27.3} calc_eirp=22.68 diff=4.62
[WARNING] gc=131 cn=73,freq_range=(6305.0, 6325.0),expected_eirp={'nominalValue': 28.2, 'upperBound': 30.2} calc_eirp=25.14 diff=5.06
[WARNING] gc=131 cn=77,freq_range=(6325.0, 6345.0),expected_eirp={'nominalValue': 28.2, 'upperBound': 30.2} calc_eirp=25.14 diff=5.06
[WARNING] gc=131 cn=81,freq_range=(6345.0, 6365.0),expected_eirp={'nominalValue': 29.2, 'upperBound': 31.2} calc_eirp=26.25 diff=4.95
[WARNING] gc=131 cn=85,freq_range=(6365.0, 6385.0),expected_eirp={'nominalValue': 29.2, 'upperBound': 31.2} calc_eirp=26.25 diff=4.95
[WARNING] gc=131 cn=89,freq_range=(6385.0, 6405.0),expected_eirp={'nominalValue': 29.3, 'upperBound': 31.3} calc_eirp=26.25 diff=5.05
[WARNING] gc=131 cn=93,freq_range=(6405.0, 6425.0),expected_eirp={'nominalValue': 29.3, 'upperBound': 31.3} calc_eirp=26.25 diff=5.05
[WARNING] gc=131 cn=125,freq_range=(6565.0, 6585.0),expected_eirp={'nominalValue': 27.6, 'upperBound': 29.6} calc_eirp=25.35 diff=4.25
[WARNING] gc=131 cn=129,freq_range=(6585.0, 6605.0),expected_eirp={'nominalValue': 27.6, 'upperBound': 29.6} calc_eirp=25.35 diff=4.25
[WARNING] gc=131 cn=133,freq_range=(6605.0, 6625.0),expected_eirp={'nominalValue': 28.1, 'upperBound': 30.1} calc_eirp=25.56 diff=4.54
[WARNING] gc=131 cn=137,freq_range=(6625.0, 6645.0),expected_eirp={'nominalValue': 28.1, 'upperBound': 30.1} calc_eirp=25.56 diff=4.54
[WARNING] gc=131 cn=149,freq_range=(6685.0, 6705.0),expected_eirp={'nominalValue': 26.4, 'upperBound': 28.4} calc_eirp=23.86 diff=4.54
[WARNING] gc=131 cn=153,freq_range=(6705.0, 6725.0),expected_eirp={'nominalValue': 26.4, 'upperBound': 28.4} calc_eirp=23.86 diff=4.54
[WARNING] gc=131 cn=165,freq_range=(6765.0, 6785.0),expected_eirp={'nominalValue': 31.0, 'upperBound': 33.0} calc_eirp=27.96 diff=5.04
[WARNING] gc=131 cn=169,freq_range=(6785.0, 6805.0),expected_eirp={'nominalValue': 23.8, 'upperBound': 25.8} calc_eirp=21.0 diff=4.80
[WARNING] gc=131 cn=173,freq_range=(6805.0, 6825.0),expected_eirp={'nominalValue': 23.8, 'upperBound': 25.8} calc_eirp=21.0 diff=4.80
[WARNING] gc=132 cn=51,freq_range=(6185.0, 6225.0),expected_eirp={'nominalValue': 28.3, 'upperBound': 30.3} calc_eirp=25.69 diff=4.61
[WARNING] gc=132 cn=75,freq_range=(6305.0, 6345.0),expected_eirp={'nominalValue': 31.2, 'upperBound': 33.2} calc_eirp=28.15 diff=5.05
[WARNING] gc=132 cn=83,freq_range=(6345.0, 6385.0),expected_eirp={'nominalValue': 32.3, 'upperBound': 34.3} calc_eirp=29.26 diff=5.04
[WARNING] gc=132 cn=91,freq_range=(6385.0, 6425.0),expected_eirp={'nominalValue': 32.3, 'upperBound': 34.3} calc_eirp=29.26 diff=5.04
[WARNING] gc=132 cn=123,freq_range=(6545.0, 6585.0),expected_eirp={'nominalValue': 30.6, 'upperBound': 32.6} calc_eirp=28.36 diff=4.24
[WARNING] gc=132 cn=131,freq_range=(6585.0, 6625.0),expected_eirp={'nominalValue': 30.6, 'upperBound': 32.6} calc_eirp=28.36 diff=4.24
[WARNING] gc=132 cn=155,freq_range=(6705.0, 6745.0),expected_eirp={'nominalValue': 29.4, 'upperBound': 31.4} calc_eirp=26.88 diff=4.52
[WARNING] gc=132 cn=163,freq_range=(6745.0, 6785.0),expected_eirp={'nominalValue': 34.0, 'upperBound': 36.0} calc_eirp=30.97 diff=5.03
[WARNING] gc=132 cn=171,freq_range=(6785.0, 6825.0),expected_eirp={'nominalValue': 26.8, 'upperBound': 28.8} calc_eirp=24.01 diff=4.79
[WARNING] gc=133 cn=167,freq_range=(6745.0, 6825.0),expected_eirp={'nominalValue': 29.8, 'upperBound': 31.8} calc_eirp=27.02 diff=4.78
[INFO] Time in Milliseconds 2:03:57.419061, processed_count=3027960
AEgbert commented 8 months ago

Which incumbent is limiting your response on 6870-6875 MHz? Since it is the far upper end of the band, I'm wondering if the difference may be the same as in #52.

alexcpn commented 8 months ago

@AEgbert with the handling as mentioned in #52 SIP2 is also now matching

centre frequency I am taking as _(frequency_assigned + frequencyupperband)/2 . I tried first (center_freq= frequency_assigned + bandwidth_Mhz/2) but that is not correct for some cases like in SIP.10

[INFO] Expected Frequency
[INFO] 5925-5930,upperBound:23.0 nominalValue:23.0
[INFO] 5930-5949,upperBound:11.1 nominalValue:9.1
[INFO] 5949-6050,upperBound:11.2 nominalValue:9.2
[INFO] 6050-6078,upperBound:23.0 nominalValue:23.0
[INFO] 6078-6109,upperBound:-0.4 nominalValue:-2.4
[INFO] 6109-6137,upperBound:23.0 nominalValue:23.0
[INFO] 6137-6168,upperBound:-0.3 nominalValue:-2.3
[INFO] 6168-6211,upperBound:23.0 nominalValue:23.0
[INFO] 6211-6241,upperBound:14.3 nominalValue:12.3
[INFO] 6241-6302,upperBound:7.1 nominalValue:5.1
[INFO] 6302-6331,upperBound:17.2 nominalValue:15.2
[INFO] 6331-6359,upperBound:20.9 nominalValue:18.9
[INFO] 6359-6392,upperBound:18.2 nominalValue:16.2
[INFO] 6392-6420,upperBound:18.3 nominalValue:16.3
[INFO] 6420-6425,upperBound:23.0 nominalValue:23.0
[INFO] 6525-6540,upperBound:23.0 nominalValue:23.0
[INFO] 6540-6570,upperBound:23.0 nominalValue:22.9
[INFO] 6570-6580,upperBound:23.0 nominalValue:23.0
[INFO] 6580-6590,upperBound:16.6 nominalValue:14.6
[INFO] 6590-6600,upperBound:23.0 nominalValue:22.7
[INFO] 6600-6610,upperBound:23.0 nominalValue:23.0
[INFO] 6610-6640,upperBound:17.1 nominalValue:15.1
[INFO] 6640-6650,upperBound:23.0 nominalValue:23.0
[INFO] 6676-6680,upperBound:19.9 nominalValue:17.9
[INFO] 6680-6694,upperBound:23.0 nominalValue:21.3
[INFO] 6694-6700,upperBound:23.0 nominalValue:21.4
[INFO] 6700-6710,upperBound:15.4 nominalValue:13.4
[INFO] 6710-6770,upperBound:23.0 nominalValue:23.0
[INFO] 6770-6800,upperBound:20.0 nominalValue:18.0
[INFO] 6800-6803,upperBound:23.0 nominalValue:23.0
[INFO] 6803-6808,upperBound:12.8 nominalValue:10.8
[INFO] 6808-6810,upperBound:23.0 nominalValue:23.0
[INFO] 6810-6820,upperBound:23.0 nominalValue:21.0
[INFO] 6820-6850,upperBound:23.0 nominalValue:23.0
[INFO] 6850-6860,upperBound:8.8 nominalValue:6.8
[INFO] 6860-6870,upperBound:17.1 nominalValue:15.1
[INFO] 6870-6875,upperBound:23.0 nominalValue:23.0
[INFO] Calculated Frequency
[INFO] 5925.0-5930.025  psd: 23.0 ok
[INFO] 5930.025-5990.025  psd: 9.162855109773304 ok
[INFO] 5990.025-6049.325  psd: 9.248849335298857 ok one range
[INFO] 6049.325-6078.45  psd: 21.725732881676123 ok
[INFO] 6078.45-6108.45  psd: -3.893601986183313 ok
[INFO] 6108.45-6137.75  psd: 23.0 ok
[INFO] 6137.75-6167.75  psd: -3.7932845988204917 ok
[INFO] 6167.75-6167.925  psd: 23.0 ok 
[INFO] 6167.925-6182.065  psd: 23.0 ok 
[INFO] 6182.065-6211.89  psd: 23.0 ok (single range)
[INFO] 6211.89-6241.365  psd: 9.674139423097031 ok
[INFO] 6241.365-6241.54  psd: 5.107086117782345
[INFO] 6241.54-6271.54  psd: 5.0865290694984395
[INFO] 6271.54-6301.365  psd: 5.107086117782345 ok
[INFO] 6301.365-6330.84  psd: 12.130862002759528 ok
[INFO] 6330.84-6359.965  psd: 15.87044751566225 ok
[INFO] 6359.965-6419.965  psd: 13.239783921163593 ok
[INFO] 6419.965-6425.0  psd: 23.0 ok
[INFO] 6525.0-6540.0  psd: 23.0 ok
[INFO] 6540.0-6570.0  psd: 20.05168643652047 ok
[INFO] 6570.0-6580.0  psd: 23.0 ok 
[INFO] 6580.0-6590.0  psd: 12.34298884470428 ok 
[INFO] 6590.0-6600.0  psd: 22.184274075964964 ok 
[INFO] 6600.0-6610.0  psd: 20.232672164996245 ok
[INFO] 6610.0-6640.0  psd: 12.549264481357142 ok
[INFO] 6640.0-6650.0  psd: 23.0 ok
[INFO] 6650.0-6675.2  psd: 0 ok 
[INFO] 6675.2-6680.0  psd: 15.143535220560409 ok
[INFO] 6680.0-6700.0  psd: 20.139941542990556 ok
[INFO] 6700.0-6710.0  psd: 10.854650405008215 ok
[INFO] 6710.0-6770.0  psd: 23.0 ok
[INFO] 6770.0-6800.0  psd: 14.952141274939152 ok
[INFO] 6800.0-6803.75  psd: 23.0 ok
[INFO] 6803.75-6807.5  psd: 7.986306331845867 ok
[INFO] 6807.5-6810.0  psd: 23.0 ok
[INFO] 6810.0-6820.0  psd: 20.964231584415444 ok
[INFO] 6820.0-6830.0  psd: 23.0
[INFO] 6830.0-6850.0  psd: 20.277231620836176 ok
[INFO] 6850.0-6860.0  psd: 5.7319583598293065 ok
[INFO] 6860.0-6870.0  psd: 12.080383470008258 ok
[INFO] 6870.0-6872.5  psd: 23.0 ok
[INFO] 6872.5-6875.0  psd: 23.0 ok
[WARNING] gc=131 cn=53,freq_range=(6205.0, 6225.0),expected_eirp={'nominalValue': 25.3, 'upperBound': 27.3} calc_eirp=22.68 diff=2.62
[WARNING] gc=131 cn=73,freq_range=(6305.0, 6325.0),expected_eirp={'nominalValue': 28.2, 'upperBound': 30.2} calc_eirp=25.14 diff=3.06
[WARNING] gc=131 cn=77,freq_range=(6325.0, 6345.0),expected_eirp={'nominalValue': 28.2, 'upperBound': 30.2} calc_eirp=25.14 diff=3.06
[WARNING] gc=131 cn=81,freq_range=(6345.0, 6365.0),expected_eirp={'nominalValue': 29.2, 'upperBound': 31.2} calc_eirp=26.25 diff=2.95
[WARNING] gc=131 cn=85,freq_range=(6365.0, 6385.0),expected_eirp={'nominalValue': 29.2, 'upperBound': 31.2} calc_eirp=26.25 diff=2.95
[WARNING] gc=131 cn=89,freq_range=(6385.0, 6405.0),expected_eirp={'nominalValue': 29.3, 'upperBound': 31.3} calc_eirp=26.25 diff=3.05
[WARNING] gc=131 cn=93,freq_range=(6405.0, 6425.0),expected_eirp={'nominalValue': 29.3, 'upperBound': 31.3} calc_eirp=26.25 diff=3.05
[WARNING] gc=131 cn=117,freq_range=(6525.0, 6545.0),expected_eirp={'nominalValue': 35.9, 'upperBound': 36.0} calc_eirp=33.06 diff=2.84
[WARNING] gc=131 cn=121,freq_range=(6545.0, 6565.0),expected_eirp={'nominalValue': 35.9, 'upperBound': 36.0} calc_eirp=33.06 diff=2.84
[WARNING] gc=131 cn=125,freq_range=(6565.0, 6585.0),expected_eirp={'nominalValue': 27.6, 'upperBound': 29.6} calc_eirp=25.35 diff=2.25
[WARNING] gc=131 cn=129,freq_range=(6585.0, 6605.0),expected_eirp={'nominalValue': 27.6, 'upperBound': 29.6} calc_eirp=25.35 diff=2.25
[WARNING] gc=131 cn=133,freq_range=(6605.0, 6625.0),expected_eirp={'nominalValue': 28.1, 'upperBound': 30.1} calc_eirp=25.56 diff=2.54
[WARNING] gc=131 cn=137,freq_range=(6625.0, 6645.0),expected_eirp={'nominalValue': 28.1, 'upperBound': 30.1} calc_eirp=25.56 diff=2.54
[WARNING] gc=131 cn=149,freq_range=(6685.0, 6705.0),expected_eirp={'nominalValue': 26.4, 'upperBound': 28.4} calc_eirp=23.86 diff=2.54
[WARNING] gc=131 cn=153,freq_range=(6705.0, 6725.0),expected_eirp={'nominalValue': 26.4, 'upperBound': 28.4} calc_eirp=23.86 diff=2.54
[WARNING] gc=131 cn=165,freq_range=(6765.0, 6785.0),expected_eirp={'nominalValue': 31.0, 'upperBound': 33.0} calc_eirp=27.96 diff=3.04
[WARNING] gc=131 cn=169,freq_range=(6785.0, 6805.0),expected_eirp={'nominalValue': 23.8, 'upperBound': 25.8} calc_eirp=21.0 diff=2.80
[WARNING] gc=131 cn=173,freq_range=(6805.0, 6825.0),expected_eirp={'nominalValue': 23.8, 'upperBound': 25.8} calc_eirp=21.0 diff=2.80
[WARNING] gc=131 cn=177,freq_range=(6825.0, 6845.0),expected_eirp={'nominalValue': 36.0, 'upperBound': 36.0} calc_eirp=33.29 diff=2.71
[WARNING] gc=132 cn=51,freq_range=(6185.0, 6225.0),expected_eirp={'nominalValue': 28.3, 'upperBound': 30.3} calc_eirp=25.69 diff=2.61
[WARNING] gc=132 cn=75,freq_range=(6305.0, 6345.0),expected_eirp={'nominalValue': 31.2, 'upperBound': 33.2} calc_eirp=28.15 diff=3.05
[WARNING] gc=132 cn=83,freq_range=(6345.0, 6385.0),expected_eirp={'nominalValue': 32.3, 'upperBound': 34.3} calc_eirp=29.26 diff=3.04
[WARNING] gc=132 cn=91,freq_range=(6385.0, 6425.0),expected_eirp={'nominalValue': 32.3, 'upperBound': 34.3} calc_eirp=29.26 diff=3.04
[WARNING] gc=132 cn=123,freq_range=(6545.0, 6585.0),expected_eirp={'nominalValue': 30.6, 'upperBound': 32.6} calc_eirp=28.36 diff=2.24
[WARNING] gc=132 cn=131,freq_range=(6585.0, 6625.0),expected_eirp={'nominalValue': 30.6, 'upperBound': 32.6} calc_eirp=28.36 diff=2.24
[WARNING] gc=132 cn=155,freq_range=(6705.0, 6745.0),expected_eirp={'nominalValue': 29.4, 'upperBound': 31.4} calc_eirp=26.88 diff=2.52
[WARNING] gc=132 cn=163,freq_range=(6745.0, 6785.0),expected_eirp={'nominalValue': 34.0, 'upperBound': 36.0} calc_eirp=30.97 diff=3.03
[WARNING] gc=132 cn=171,freq_range=(6785.0, 6825.0),expected_eirp={'nominalValue': 26.8, 'upperBound': 28.8} calc_eirp=24.01 diff=2.79
[WARNING] gc=133 cn=87,freq_range=(6345.0, 6425.0),expected_eirp={'nominalValue': 35.3, 'upperBound': 36.0} calc_eirp=32.27 diff=3.03
[WARNING] gc=133 cn=167,freq_range=(6745.0, 6825.0),expected_eirp={'nominalValue': 29.8, 'upperBound': 31.8} calc_eirp=27.02 diff=2.78
[INFO] Time in Milliseconds 1:05:54.089654, processed_count=3009240