Joint Channel Identification and Estimation in Wireless Network: Sparsity and Optimization - 2018


In this Project, we have a tendency to study channel identification for a wireless network with the help of compressed sensing (CS) in both cases of known and unknown sparsity levels of clusters. For the unknown case, we have a tendency to propose using blind CS signal recovery algorithm to sequentially estimate each sparsity level and channel gains. The refined version of blind CS technique is additionally provided to boost the consistency of channel identification process. The convergence of two algorithms is guaranteed by making certain that the value functions decrease after every update. We then investigate the cluster sparsity of users in the case of known sparsity levels of all clusters. By exploiting the alternating direction method of multipliers algorithm through distributed optimization, we have a tendency to can determine channels in sparse clusters parallelly and efficiently compared with conventional convex techniques. In summary, this Project provides some insight into using sparse and distributed algorithms to efficiently solve the matter of quick channel identification and estimation of users in future network.

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