Improving Fairness for Cell-Free Massive MIMO Through Interference-Aware Massive Access

TL;DR

This paper introduces an interference-aware massive access scheme for cell-free massive MIMO that optimizes access point-user association and pilot assignment, significantly improving fairness and spectral efficiency.

Contribution

It proposes a novel interference-aware reward metric and iterative algorithms for joint AP-UE association and pilot assignment in CF mMIMO, enhancing performance.

Findings

IAMA outperforms benchmark schemes in fairness.

IAMA improves average spectral efficiency.

The scheme effectively manages interference in dense scenarios.

Abstract

Cell-free massive multiple-input multiple-output (CF mMIMO) provides good interference management by coordinating many more access points (APs) than user equipments (UEs). It becomes challenging to determine which APs should serve which UEs with which pilots when the number of UEs approximates the number of APs and far exceeds the number of pilots. Compared to the previous work, a better compromise between spectral efficiency (SE) and implementation simplicity is needed in such massive access scenarios. This paper proposes an interference-aware massive access (IAMA) scheme realizing joint AP-UE association and pilot assignment for CF mMIMO by exploiting the large-scale interference features. We propose an interference-aware reward as a novel performance metric and use it to develop two iterative algorithms to optimize the association and pilot assignment. The numerical results show a prominent advantage of our IAMA scheme over the benchmark schemes in terms of the user fairness and the average SE.