Study of Graph-Based Search for Energy-Efficient Clustering in Cell-Free Massive MIMO Networks

Abstract

This paper investigates energy-efficient clustering in user-centric cell-free massive MIMO networks, addressing the access point clustering and power allocation problems via a mixed-integer fractional program. We propose a framework for energy-efficient clustering and power allocation with a graph-based structured search and describe its optimum solution via an exhaustive search. We also develop the Graph-Based Steepest Ascent (GBSA) algorithm, which combines a graph-based structured search along with continuous power allocation via fractional programming. The proposed GBSA algorithm achieves linear per-iteration complexity while reaching energy efficiency close to the global optimum, outperforming competing techniques and offering a scalable solution for future networks.