🤖 AI Summary
This work addresses energy efficiency optimization in user-centric cell-free massive MIMO networks by proposing a unified framework that jointly performs access point clustering and power allocation, integrating graph structure search with fractional programming techniques. The core contribution is the development of a Graph-Based Steepest Ascent (GBSA) algorithm, which efficiently solves the original mixed-integer fractional programming problem with linear per-iteration complexity while achieving near-global-optimal energy efficiency. Experimental results demonstrate that GBSA significantly outperforms existing methods in terms of energy efficiency, closely approaching the exhaustive-search optimum, and exhibits strong scalability across network sizes.
📝 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.