Compressive Channel Estimation and Multi-user Detection in C-RAN

TL;DR

This paper introduces a compressed sensing-based approach for efficient channel estimation and multi-user detection in C-RAN, leveraging sparsity to reduce overhead and improve processing speed.

Contribution

It proposes a novel mixed L2,1-regularization functional for sparse recovery and an efficient, convergent algorithm based on ADMM for C-RAN applications.

Findings

Effective reduction in pilot overhead.

Improved detection accuracy with the proposed method.

Algorithm demonstrates guaranteed convergence.

Abstract

This paper considers the channel estimation (CE) and multi-user detection (MUD) problems in cloud radio access network (C-RAN). Assuming that active users are sparse in the network, we solve CE and MUD problems with compressed sensing (CS) technology to greatly reduce the long identification pilot overhead. A mixed L{2,1}-regularization functional for extended sparse group-sparsity recovery is proposed to exploit the inherently sparse property existing both in user activities and remote radio heads (RRHs) that active users are attached to. Empirical and theoretical guidelines are provided to help choosing tuning parameters which have critical effect on the performance of the penalty functional. To speed up the processing procedure, based on alternating direction method of multipliers and variable splitting strategy, an efficient algorithm is formulated which is guaranteed to be convergent. Numerical results are provided to illustrate the effectiveness of the proposed functional and efficient algorithm.