DRL-based Distributed Resource Allocation for Edge Computing in Cell-Free Massive MIMO Network

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

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)

Abstract

In this paper, with the aim of addressing the stringent computing and quality-of-service (QoS) requirements of recently introduced advanced multimedia services, we consider a cell-free massive MIMO-enabled mobile edge network. In particular, benefited from the reliable cell-free links to offload intensive computation to the edge server, resource-constrained end-users can augment on-board (local) processing with edge computing. To this end, we formulate a joint communication and computing resource allocation (JCCRA) problem to minimize the total energy consumption of the users, while meeting the respective user-specific deadlines. To tackle the problem, we propose a distributed solution approach based on cooperative multi-agent reinforcement learning framework, wherein each user is implemented as a learning agent to make joint resource allocation relying on local information only. The simulation results demonstrate that the performance of the proposed distributed approach outperforms the heuristic baselines, converging to a centralized target benchmark, without resorting to large over-head. Moreover, we showed that the proposed algorithm has performed significantly better in cell-free system as compared with the cellular MEC systems, e.g., a small cell-based MEC system.

Original languageEnglish
Pages (from-to)3845-3850
Number of pages6
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
DOIs
Publication statusPublished - 2022
Event2022 IEEE Global Communications Conference, GLOBECOM 2022 - Virtual, Online, Brazil
Duration: 2022 Dec 42022 Dec 8

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • cell-free massive MIMO
  • deep reinforcement learning (DRL)
  • edge computing
  • joint communication and computing resource allocation
  • multi-agent reinforcement learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing

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