Study of Clustering Techniques and Scheduling Algorithms with Fairness for Cell-Free MIMO Networks

TL;DR

This paper introduces a novel clustering method based on information rates for cell-free MIMO networks, along with a fair resource allocation scheme, demonstrating improved performance over existing methods.

Contribution

It presents a new information rate-based clustering technique and a fairness-aware scheduling algorithm for cell-free MIMO networks, outperforming prior approaches.

Findings

Proposed clustering outperforms existing methods.

Fair scheduling improves user fairness.

Numerical results confirm performance gains.

Abstract

In this work, we propose a clustering technique based on information rates for cell-free massive multiple-input multiple-output (MIMO) networks. Unlike existing clustering approaches that rely on the large scale fading coefficients of the channels and user-centric techniques, we develop an approach that is based on the information rates of cell-free massive MIMO networks. We also devise a resource allocation technique to incorporate the proposed clustering and schedule users with fairness. An analysis of the proposed clustering approach based on information rates is carried out along with an assessment of its benefits for scheduling. Numerical results show that the proposed techniques outperform existing approaches.