Optimized Backhaul Compression for Uplink Cloud Radio Access Network
Yuhan Zhou, Wei Yu

TL;DR
This paper proposes an optimized quantization scheme for uplink C-RAN that maximizes sum rate under backhaul constraints, using an efficient alternating convex optimization approach and near-optimal noise level settings.
Contribution
It introduces a novel optimization method for quantization noise levels in uplink C-RAN, demonstrating near-optimality and constant-gap capacity results with practical coding schemes.
Findings
Optimized quantization noise levels significantly improve network performance.
The proposed approach achieves near-capacity sum-rate with Wyner-Ziv coding.
Efficient backhaul capacity allocation enhances multicell and heterogeneous network throughput.
Abstract
This paper studies the uplink of a cloud radio access network (C-RAN) where the cell sites are connected to a cloud-computing-based central processor (CP) with noiseless backhaul links with finite capacities. We employ a simple compress-and-forward scheme in which the base-stations(BSs) quantize the received signals and send the quantized signals to the CP using either distributed Wyner-Ziv coding or single-user compression. The CP decodes the quantization codewords first, then decodes the user messages as if the remote users and the cloud center form a virtual multiple-access channel (VMAC). This paper formulates the problem of optimizing the quantization noise levels for weighted sum rate maximization under a sum backhaul capacity constraint. We propose an alternating convex optimization approach to find a local optimum solution to the problem efficiently, and more importantly,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
