Cloud radio networks optimize data compression for limited links
Fronthaul Compression for Uplink Cloud-RAN with Finite-Alphabet Inputs: A Reverse Mercury/Waterfilling Approach
Information Theory
Summary
Cloud radio access networks connect many antennas to a central hub to manage wireless signals better, but the data links between them have limited capacity. Traditional methods for compressing signals assume they come from signals like Gaussian noise, but real signals use a limited set of symbols that behave differently. The authors developed a new way to allocate bits for signal compression that matches the real signals' properties, helping maintain good data rates even when the link capacity is tight. Their method also works well as the number of antennas grows larger, which is important for modern networks.
What this means in practice
- •For wireless network engineers: Use the method to manage signal compression in cloud radio setups when fronthaul links have limited capacity, preserving data rates.
- •For antenna system designers: Optimize bit allocation in systems with many antennas where fronthaul capacity is a bottleneck, improving uplink signal quality.
Authors
Subin Shin, Jaehoon Lee, Seok-Hwan Park, Jeonghun Park
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.