AI 中文总结
本文针对无小区大规模MIMO网络的AP开关优化问题，提出约束遗传算法与帕累托驱动遗传算法两种进化策略，经仿真验证其能效及能效-频谱效率权衡性能优于现有贪心基准。
AI 中文摘要
无小区大规模多输入多输出（CF-mMIMO）是下一代无线系统的新兴技术，在时变程度高、空间非均匀的业务负载下，动态调整活跃接入点（AP）集合对平衡服务质量（QoS）需求与网络能耗至关重要。现有AP开关机制通常基于最坏情况定界或贪心启发式算法，对组合激活空间的探索不足，导致能效结果次优。本文提出两种专为CF-mMIMO网络设计的进化式AP选择策略：第一种为约束遗传算法（CGA），针对任意固定激活基数确定活跃AP的近优子集，外层搜索则确定全局最优工作点；第二种为帕累托驱动遗传算法（PDGA），通过在所有可行激活模式上演化帕累托前沿，联合优化频谱效率与能效。本文对两种技术均进行了详细的计算复杂度分析。在空间非均匀的实际业务场景下，结合共轭波束成形（CB）和最小均方误差（MMSE）处理开展的仿真验证了持续的性能增益，所提方法始终优于当前最优的贪心基准方案，为CB和MMSE方案均带来了显著的能效提升，同时优化了通常难以在其他方面不产生损失的能效-频谱效率权衡关系，这些结果凸显了进化优化作为高能效CF-mMIMO部署的强大可靠方法的巨大潜力。
英文摘要
Cell-free massive multiple input multiple output (CF-mMIMO) is an emerging technology for next-generation wireless systems, where dynamically adapting the set of active access points (APs) is crucial to balance quality of service (QoS) requirements and network energy consumption under highly time-varying and spatially non-uniform traffic loads. Existing AP ON/OFF mechanisms--typically based on worst-case dimensioning or greedy heuristics--explore the combinatorial activation space inadequately, leading to suboptimal energy-efficiency outcomes. This paper introduces two evolutionary AP-selection strategies tailored to CF-mMIMO networks. The first, a constrained genetic algorithm (CGA), identifies the near-optimal subset of active APs for any fixed activation cardinality, while an outer search determines the globally optimal operating point. The second, a Pareto-driven genetic algorithm (PDGA), jointly optimizes spectral and energy efficiency by evolving a Pareto front over all feasible activation patterns. A detailed computational-complexity analysis is provided for both techniques. Simulations conducted under realistic spatially inhomogeneous traffic and considering both conjugate beamforming (CB) and minimum mean square error (MMSE) processing confirm consistent performance gains. The proposed methods consistently outperform state-of-the-art greedy benchmarks, delivering noticeable improvements in energy efficiency for both CB and MMSE schemes, while simultaneously enhancing the energy-spectral efficiency tradeoff, which is typically difficult to improve without incurring penalties elsewhere. These results highlight the strong potential of evolutionary optimization as a powerful and reliable approach for energy-efficient CF-mMIMO deployments.
CommentsAuthor Accepted Manuscript. Published in IEEE Open Journal of the Communications Society. Available at https://doi.org/10.1109/OJCOMS.2026.3717118. Copyright 2026 IEEE
DOI：10.1109/OJCOMS.2026.3717118
