Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks
From MaRDI portal
Publication:6349078
arXiv2009.06681MaRDI QIDQ6349078
Author name not available (Why is that?)
Publication date: 14 September 2020
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.
Has companion code repository: https://github.com/sinannasir/Power-Control-asilomar
This page was built for publication: Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6349078)
