In this project, we consider the problem of network parameter optimization
for rate maximization. We frame this as a joint optimization problem of power
control, beam forming, and interference cancellation. We consider the setting
where multiple Base Stations (BSs) are communicating with multiple user
equipments (UEs). Because of the exponential computational complexity of brute
force search, we instead solve this non-convex optimization problem using deep
reinforcement learning (RL) techniques. The modern communication systems are
notorious for their difficulty in exactly modeling their behaviour. This limits
us in using RL based algorithms as interaction with the environment is needed
for the agent to explore and learn efficiently. Further, it is ill advised to
deploy the algorithm in real world for exploration and learning because of the
high cost of failure. In contrast to the previous RL-based solutions proposed,
such as deep-Q network (DQN) based control, we propose taking an offline model
based approach. We specifically consider discrete batch constrained deep
Q-learning (BCQ) and show that performance similar to DQN can be acheived with
only a fraction of the data and without the need for exploration. This results
in maximizing sample efficiency and minimizing risk in the deployment of a new
algorithm to commercial networks. We provide the entire resource of the
project, including code and data, at the following link:
this https URL safe-rl-deployment-for-5g.

通过深度强化学习技术解决网络参数优化问题，实现速率最大化，涉及功率控制、波束成形和干扰抵消等方面，以提高采样效率和降低算法在商业网络中的风险。