Sparse Active User Detection and Channel Estimation Using ADMM in Uplink C-RAN

Abstract

We consider active user detection (AUD) and channel estimation (CE) in the uplink of a cloud radio access network (C-RAN) with sparse active users. Since it is difficult to obtain the prior knowledge of the sparsity level and the measurement matrix will not always satisfy the restricted isometry property (RIP), it is not effective to use the conventional compressed sensing (CS) techniques directly. Due to the large number of users in the C-RAN, directly solving the active user detection problem will involve high complexity. We propose a new algorithm called D-ADMM by using the alternating direction method of multipliers (ADMM)to conduct AUD and CE, which does not need the prior knowledge of the sparsity level. Compared with the standard convex optimization algorithm (cvx), the proposed D-ADMM algorithm can reduce the processing time by a factor of 7/8 while achieving the same final results.