Sparse Activation for Sustainable Cell-Free Massive MIMO Networks: Less is More

2026-06-02

AI Summary

This work addresses the joint optimization of energy efficiency and spectral efficiency in cell-free massive MIMO networks for 6G by proposing a structured sparsity-aware activation framework based on an antenna-level optimal bilinear equalizer (OBE). The approach extends large-scale fading decoding from scalar coefficients to antenna-aware matrix weighting and integrates a hierarchical sparsity model with a tree-structured proximal operator to enable array-level sparse activation. This design simultaneously minimizes mean square error while balancing user-specific and network-wide objectives. Experimental results demonstrate that the proposed method significantly enhances spectral efficiency—particularly as the number of antennas grows—and achieves substantial power savings with only a controllable loss in spectral performance, thereby markedly improving overall system energy efficiency.

Abstract

Motivated by the vision of making sixth-generation (6G) networks sustainable, we study the sparse antenna/array activation problems in uplink cell-free massive multiple-input multiple-output (CF mMIMO) networks. We first develop an antenna-level optimal bilinear equalizer (OBE) weighting framework, in which each access point-user equipment (AP-UE) pair is assigned a matrix-valued long-term weight to shape the contribution of individual antenna elements, thereby generalizing the conventional large-scale fading decoding (LSFD) strategy from scalar coefficients to antenna-element-aware weighting. Building on this structure, we formulate sparse antenna activation as structured sparsity-inducing mean square error (MSE) minimization problems, and design four activation schemes at two granularities: antenna-level and array-level, each with UE-specific and network-wide (all-UEs) variants. The resulting convex problems are solved efficiently via the proximal method with closed-form group-wise updates, while the network-wide schemes are modeled through hierarchical sparsity and handled by a tree-structured proximal operator. Numerical results under correlated Rician channels and a detailed power consumption model demonstrate that the OBE weighting scheme consistently improves spectral efficiency over the LSFD, with gains increasing with the number of antennas. Meanwhile, the studied sparse activation schemes can achieve substantial energy efficiency improvement and power reduction with controllable spectral efficiency loss.

Problem

• sparse activation
• cell-free massive MIMO
• energy efficiency
• sustainable 6G networks
• antenna selection

Innovation

• sparse activation
• optimal bilinear equalizer
• cell-free massive MIMO
• structured sparsity
• proximal optimization