发表机构
Yonsei University; Hanyang University（延世大学; 汉阳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对有限字母输入的上行云无线接入网，提出反向汞/注水比特分配方法，最大化LMMSE检测后的广义互信息，在紧张fronthaul预算下维持端到端速率并具鲁棒性。
AI 中文摘要
云无线接入网（C-RAN）通过在集中式单元（CU）处联合处理分布式远端单元（RU）的观测来减轻小区间干扰，但有限的fronthaul容量迫使每个RU对其接收信号进行压缩。在变换-压缩-转发方案下，RU对其信号进行变换并对所得系数进行量化，通过比特分配在系数间分配有限的比特预算。经典的反向注水假设高斯信源，然而实际的有限字母符号携带的互信息在$\log_2 M$处饱和，这使得此类输入的比特分配问题尚未解决。我们通过将比特分配表述为最大化在CU处进行线性MMSE（LMMSE）检测后所达到的有限字母广义互信息（GMI）来解决这一问题。通过I-MMSE关系，这产生了一个定点更新，其收敛解分解为一个容器高度、一个共享水位和一个有限字母汞水平；我们将其称为反向汞/注水（RMWF）。数值结果表明，在紧张的fronthaul预算下，RMWF维持端到端速率，并在天线扩展下保持鲁棒性，这在现代C-RAN中天线数量超过fronthaul容量时变得越来越重要。
英文摘要
The cloud radio access network (C-RAN) mitigates inter-cell interference by jointly processing the observations of distributed remote units (RUs) at a centralized unit (CU), but limited fronthaul capacity forces each RU to compress its received signal. Under transform-compress-forward, an RU transforms its signal and quantizes the resulting coefficients, with bit allocation distributing a finite bit budget across them. Classical reverse waterfilling assumes Gaussian sources, yet practical finite-alphabet symbols carry mutual information that saturates at $\log_2 M$, leaving bit allocation for such inputs unresolved. We address this by formulating bit allocation as maximizing the finite-alphabet generalized mutual information (GMI) achieved after linear MMSE (LMMSE) detection at the CU. Via the I-MMSE relation, this yields a fixed-point update whose converged solution decomposes into a vessel height, a shared water level, and a finite-alphabet mercury level; we term it {reverse mercury/waterfilling} (RMWF). Numerical results show that RMWF sustains end-to-end rate under tight fronthaul budgets and remains robust under antenna scaling, which is increasingly consequential as antenna counts outpace fronthaul capacity in modern C-RAN.
