Distributed Fronthaul Compression and Joint Signal Recovery in Cloud-RAN
Xiongbin Rao, and Vincent K. N. Lau

TL;DR
This paper proposes a distributed fronthaul compression and joint signal recovery method for uplink C-RAN systems, effectively reducing fronthaul load while maintaining high signal recovery performance through distributed compressive sensing techniques.
Contribution
It introduces a novel distributed compressive sensing approach that accounts for multi-access fading, enabling efficient end-to-end signal recovery in C-RAN architectures.
Findings
The aggregate measurement matrix satisfies the restricted isometry property with high probability.
Tradeoff analysis between uplink capacity and fronthaul loading is established.
The proposed method improves scalability of C-RAN by reducing fronthaul requirements.
Abstract
The cloud radio access network (C-RAN) is a promising network architecture for future mobile communications, and one practical hurdle for its large scale implementation is the stringent requirement of high capacity and low latency fronthaul connecting the distributed remote radio heads (RRH) to the centralized baseband pools (BBUs) in the C-RAN. To improve the scalability of C-RAN networks, it is very important to take the fronthaul loading into consideration in the signal detection, and it is very desirable to reduce the fronthaul loading in C-RAN systems. In this paper, we consider uplink C-RAN systems and we propose a distributed fronthaul compression scheme at the distributed RRHs and a joint recovery algorithm at the BBUs by deploying the techniques of distributed compressive sensing (CS). Different from conventional distributed CS, the CS problem in C-RAN system needs to…
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