Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks

• URL: http://arxiv.org/abs/2009.06681v1
• Date: Mon, 14 Sep 2020 18:29:12 GMT
• Title: Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks
• Authors: Yasar Sinan Nasir and Dongning Guo
• Abstract summary: Deep reinforcement learning offers a model-free alternative to supervised deep learning and classical optimization. We present a distributively executed continuous power control algorithm with the help of deep actor-critic learning. We integrate the proposed power control algorithm to a time-slotted system where devices are mobile and channel conditions change rapidly.
• Score: 5.930707872313038
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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