Compressive Channel Estimation and Multi-User Detection in C-RAN with Low-Complexity Methods

Qi He, Tony Q.S. Quek, Zhi Chen, Qi Zhang, Shaoqian Li

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

This paper considers the channel estimation (CE) and multi-user detection (MUD) problems in cloud radio access network (C-RAN). By taking into account of the sparsity of user activities in C-RAN, we solve the CE and MUD problems with compressed sensing to greatly reduce the large pilot overhead. A mixed $\ell -{2,1}$ -regularization penalty functional is proposed to exploit the inherent sparsity existing in both the user activities and remote radio heads with which active users are associated. An iteratively re-weighted strategy is adopted to further enhance the estimation accuracy, and empirical and theoretical guidelines are also provided to assist in choosing tuning parameters. To speed up the optimization procedure, three low-complexity methods under different computing setups are proposed to provide differentiated services. With a centralized setting at the baseband unit pool, we propose a sequential method based on block coordinate descent (BCD). With a modern distributed computing setup, we propose two parallel methods based on alternating direction method of multipliers (ADMM) and hybrid BCD (HBCD), respectively. Specifically, the ADMM is guaranteed to converge but has a high computational complexity, while the HBCD has low complexity but works under empirical guidance. Numerical results are provided to verify the effectiveness of the proposed functional and methods.

Original languageEnglish
Pages (from-to)3931-3944
Number of pages14
JournalIEEE Transactions on Wireless Communications
Volume17
Issue number6
DOIs
Publication statusPublished - 2018 Jun

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Compressive Channel Estimation and Multi-User Detection in C-RAN with Low-Complexity Methods'. Together they form a unique fingerprint.

Cite this