发表机构
University of Toronto; Ericsson Canada（多伦多大学; 爱立信加拿大）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对前传受限无蜂窝MIMO网络，提出一种仅依赖大尺度衰落的可扩展失真感知聚类方法，通过多项式时间算法达到全局最优，性能优于现有方案。
AI 中文摘要
本文研究了采用最大比合并（MRC）的上行链路前传受限无蜂窝MIMO网络中的失真感知聚类问题。虽然MRC因其低复杂度而具有吸引力，但其性能受到干扰、前传失真和信道不完善的影响。这促使了通过接入点协调来补偿这些损害的聚类策略。由于基于瞬时小尺度衰落的优化在大规模系统中不可扩展，并且由于频繁的信道变化而不切实际，我们转而优化一个仅依赖于大尺度衰落系数的目标。为此，使用渐近分析推导平均网络和速率的确定性等价表达式，重点关注量化失真，从而得到一个二次比线性目标。尽管在二元约束下最大化这样的目标是非凸的，我们利用其结构开发了一种多项式时间方案，能够达到全局最优。数值结果表明，所提出的聚类方法相比现有文献的改进变体提供了性能增益，并且与通过穷举搜索获得的小尺度衰落全局最优保持竞争力。
英文摘要
This paper studies distortion-aware clustering for uplink fronthaul-limited cell-free MIMO networks employing maximum ratio combining (MRC). While MRC is appealing for its low-complexity, its performance is limited by interference, fronthaul distortions, and channel imperfections. This motivates clustering strategies that compensate for these impairments through coordination of access points. Since instantaneous small-scale fading-based optimization is not scalable in large systems, and is impractical due to frequent channel variations, we instead optimize an objective depending only on large-scale fading coefficients. To this end, asymptotic analysis is used to derive deterministic equivalent expressions for the average network sum rate, with focus on quantization distortion, leading to a quadratic-over-linear objective. Although maximizing such an objective under binary constraints is non-convex, we exploit its structure to develop a polynomial time scheme that attains the global optimum. Numerical results show that the proposed clustering method provides performance gains over improved variants of existing literature, and remains competitive with the small-scale fading-based global optimum obtained via exhaustive search.
CommentsTo be presented at 2026 IEEE Globecom Workshops, 6 pages, 3 figures
