Latency-Aware Radio and Computational Resource Allocation for Industrial CF-mMIMO Networks

Abstract

Advances in communication technologies, such as cell-free massive multi-input multiple-output architecture and edge computing, are expected to support diverse heterogeneous service requirements in industrial use cases. However, the diverse communication demands of Industry 4.0 use cases make it essential to efficiently manage both radio and computational resources. This paper introduces a novel approach that addresses these challenges through a two-step sequential optimization process. First, we propose a genetic algorithm-based radio resource allocation scheme from a scheduling perspective. A second sub-problem concerning computational resource allocation is also addressed sequentially. The numerical results demonstrate that our proposed algorithm significantly improves latency compliance and effectively reduces the percentage of packets that violate deadline constraints. Considering services with small packets and strict latency constraints, the proposed scheme achieves gains of 98.5 % and 99.5 % compared to the benchmarking schemes. Furthermore, the proposed solution has shown great adaptability to varying latency requirements and traffic with respect to state-of-the-art schemes.