Joint Communication and Computing Resource Allocation over Cell-Free Massive MIMO-enabled Mobile Edge Network: A Deep Reinforcement Learning-based Approach

Fitsum Debebe Tilahun, Ameha Tsegaye Abebe, Chung G. Kang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a cell-free massive MIMO-enabled mo-edge network with the aim of meeting the stringent rements of the newly introduced multimedia services. For considered framework, we propose a distributed deep-orcement learning (DRL)-based joint communication and uting resource allocation wherein each user is implemented n independent agent to make joint resource allocation ion relying on local observation only. The simulation results nstrate that the agents learn robust policies that reduce gy consumption while attaining the ultra-low delay requires of the advanced services.

Original languageEnglish
Title of host publication3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages344-346
Number of pages3
ISBN (Electronic)9781728176383
DOIs
Publication statusPublished - 2021 Apr 13
Event3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021 - Jeju Island, Korea, Republic of
Duration: 2021 Apr 132021 Apr 16

Publication series

Name3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021

Conference

Conference3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021
CountryKorea, Republic of
CityJeju Island
Period21/4/1321/4/16

Keywords

  • cell-free massive MIMO
  • distributive deep reinforcementing
  • joint communication omputing resource allocation
  • mobile edge network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

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