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

TL;DR

This paper introduces a distributed multi-agent reinforcement learning method for joint communication and computing resource allocation in cell-free massive MIMO edge networks, improving energy efficiency and meeting QoS requirements.

Contribution

It presents a novel fully distributed solution using cooperative multi-agent reinforcement learning for resource allocation in cell-free massive MIMO edge networks.

Findings

Outperforms heuristic baselines in simulations.

Converges to centralized benchmark performance.

Performs better than cellular MEC systems.

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 fully 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 overhead. 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.