Sparse Activation for Sustainable Cell-Free Massive MIMO Networks: Less is More
2026-06-02 • Information Theory
Information Theory
AI summaryⓘ
The authors focus on improving the energy use of future 6G networks by selectively turning on antennas in large multi-antenna systems. They propose a new method to weight signals from each antenna more precisely, going beyond older techniques that treated antennas more uniformly. Using this method, they create several ways to decide which antennas or antenna groups to activate, balancing signal quality and power savings. Their tests show better data speeds with this new weighting and significant energy savings when antennas are selectively turned off, with only a small drop in performance.
6G networkscell-free massive MIMOantenna activationbilinear equalizerlarge-scale fading decodingstructured sparsitymean square error minimizationproximal methodRician channelsspectral efficiency
Authors
Zhe Wang, Shuaifei Chen, Emil Björnson
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.