Scalable Distortion-Aware Clustering for Fronthaul-Limited Cell-Free MIMO Networks

2026-10-03

AI Summary

This study addresses the performance degradation of maximum ratio combining in fronthaul-limited cell-free MIMO networks caused by interference, quantization distortion, and channel imperfections, as well as the scalability limitations of conventional small-scale fading-based clustering. To overcome these challenges, this work proposes a clustering optimization method based on large-scale fading coefficients. Through asymptotic analysis, deterministic equivalent expressions are derived to formulate a linear-fractional objective function that compensates for system impairments. Exploiting its structural properties, a polynomial-time algorithm is designed to achieve globally optimal solutions under binary constraints. Numerical results demonstrate that the proposed approach outperforms existing enhanced schemes and achieves performance comparable to exhaustive search benchmarks, effectively overcoming the scalability bottleneck inherent in instantaneous optimization.

Abstract

This paper studies distortion-aware clustering for uplink fronthaul-limited cell-free MIMO networks employing maximum ratio combining (MRC). While MRC is appealing for its low-complexity, its performance is limited by interference, fronthaul distortions, and channel imperfections. This motivates clustering strategies that compensate for these impairments through coordination of access points. Since instantaneous small-scale fading-based optimization is not scalable in large systems, and is impractical due to frequent channel variations, we instead optimize an objective depending only on large-scale fading coefficients. To this end, asymptotic analysis is used to derive deterministic equivalent expressions for the average network sum rate, with focus on quantization distortion, leading to a quadratic-over-linear objective. Although maximizing such an objective under binary constraints is non-convex, we exploit its structure to develop a polynomial time scheme that attains the global optimum. Numerical results show that the proposed clustering method provides performance gains over improved variants of existing literature, and remains competitive with the small-scale fading-based global optimum obtained via exhaustive search.

Problem

• cell-free MIMO
• fronthaul-limited
• distortion-aware clustering
• scalability
• maximum ratio combining

Innovation

• Cell-Free MIMO
• Distortion-Aware Clustering
• Deterministic Equivalent
• Polynomial-Time Algorithm
• Fronthaul-Limited