Compressive Channel Estimation and Multi-User Detection in C-RAN With Low-Complexity Methods - 2018


This Project 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 issues with compressed sensing to greatly reduce the big pilot overhead. A mixed l 2,1 -regularization penalty purposeful is proposed to take advantage of the inherent sparsity existing in each 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 help in choosing tuning parameters. To speed up the optimization procedure, 3 low-complexity methods below totally different computing setups are proposed to supply differentiated services. With a centralized setting at the baseband unit pool, we have a tendency to propose a sequential method based mostly on block coordinate descent (BCD). With a fashionable distributed computing setup, we have a tendency to propose 2 parallel strategies primarily based on alternating direction method of multipliers (ADMM) and hybrid BCD (HBCD), respectively. Specifically, the ADMM is guaranteed to converge however has a high computational complexity, while the HBCD has low complexity but works beneath empirical steerage. Numerical results are provided to verify the effectiveness of the proposed useful and ways.

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