Simulation codes for the manuscript "Deep Reinforcement Learning for Distributed Dynamic MISO Downlink-Beamforming Coordination", which has been accepted for publication in IEEE Transactions on Communications.

Requirements to run the simulation programs and plot the figures

The simulation programs require Python3 with installed packages such as Keras, Tensorflow, Numpy, Scipy and etc. The figures are plot by MATLAB.

Structure of the demo simulation program

./DRL_for_DDBC/codebook/codebook.mat the file for saving the codebook matrix used in current simulation program.

./DRL_for_DDBC/data the folder to save the simulation results of the four schemes.

./DRL_for_DDBC/rates the folder to save the achievable rate of each cell within the simulation process

./DRL_for_DDBC/base_station.py the simulator of the base station

./DRL_for_DDBC/cellular_network.py the simulator of the cellular network

./DRL_for_DDBC/channel.py the simulator of the channels

./DRL_for_DDBC/config.py the file to save the configuration of the current simulation program

./DRL_for_DDBC/data_process.py the python script to plot the simulation results of the current simulation program

./DRL_for_DDBC/dqn_for_singleagent.py the DQN agent at each BS

./DRL_for_DDBC/drl.py the DTDE DRL-based scheme

./DRL_for_DDBC/export_locations.py export the locations of BSs and UEs as .mat files

./DRL_for_DDBC/fp_algorithm.py the ideal FP approach

./DRL_for_DDBC/functions.py some extra functions requied in the simulation

./DRL_for_DDBC/greedy.py the greedy scheme

./DRL_for_DDBC/neural_network.py the file to save the configurations of the neural network in DQN

./DRL_for_DDBC/random_choose.py the random scheme

./DRL_for_DDBC/user_equipment.py the simulator of the user equipments

In each file above, there are detailed annotations to help you understand the simulation program.