Achieving $\alpha$-Fairness in Clustered Cell-Free Networking: A Tight Relaxation Approach

TL;DR

This paper introduces a tunable alpha-fairness scheme for clustered cell-free networks, balancing spectral efficiency and fairness through a novel continuous relaxation approach with proven optimality.

Contribution

It proposes a unified, tunable alpha-fairness framework with exact relaxation and efficient algorithms for clustered cell-free networking.

Findings

Achieves up to 11% improvement in Jain's fairness index

Realizes 45% gain in minimum subnetwork capacity

Maintains only 5% reduction in overall throughput

Abstract

Clustered cell-free networking has emerged as a promising architecture to balance the high performance of cell-free massive MIMO and the scalability of traditional cellular systems. However, achieving fairness across subnetworks remains a critical yet largely unsolved challenge. This paper investigates the fairness problem in clustered cell-free networking and proposes a unified and tunable alpha-fairness scheme that effectively balances overall spectral efficiency and inter-subnetwork fairness. Using the closed-form deterministic equivalent of the ergodic sum capacity, we reformulate the combinatorial clustering problem as a continuous optimization problem. Leveraging the concavity/convexity properties of the alpha-fair objective, we classify the problem into four distinct cases according to the value of alpha. For each case, we establish the exact equivalence between the original integer program and its continuous relaxation, and develop efficient algorithms with guaranteed convergence. Extensive simulations show that the proposed scheme achieves up to 11% improvement in Jain's fairness index and 45% gain in minimum subnetwork capacity, with only a negligible 5% reduction in aggregate throughput.