Problem
Research questions and friction points this paper is trying to address.
Fronthaul Compression
Finite-Alphabet Inputs
Bit Allocation
Fronthaul Compression for Uplink Cloud-RAN with Finite-Alphabet Inputs: A Reverse Mercury/Waterfilling Approach
🤖 AI Summary
该研究解决了C-RAN中有限前传容量下信号压缩问题，提出了一种基于有限字母表输入的反向汞/水平填充方法来优化比特分配。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
reverse mercury/waterfilling
finite-alphabet inputs
fronthaul compression
generalized mutual information (GMI)
linear MMSE (LMMSE)
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