Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks
This work addresses power control for scalable wireless networks, but it is incremental as it adapts existing methods to a specific domain.
The paper tackled the transmit power control problem in wireless mobile networks by proposing a distributed deep actor-critic learning algorithm, demonstrating its functionality through simulation results.
Deep reinforcement learning offers a model-free alternative to supervised deep learning and classical optimization for solving the transmit power control problem in wireless networks. The multi-agent deep reinforcement learning approach considers each transmitter as an individual learning agent that determines its transmit power level by observing the local wireless environment. Following a certain policy, these agents learn to collaboratively maximize a global objective, e.g., a sum-rate utility function. This multi-agent scheme is easily scalable and practically applicable to large-scale cellular networks. In this work, we present a distributively executed continuous power control algorithm with the help of deep actor-critic learning, and more specifically, by adapting deep deterministic policy gradient. Furthermore, we integrate the proposed power control algorithm to a time-slotted system where devices are mobile and channel conditions change rapidly. We demonstrate the functionality of the proposed algorithm using simulation results.