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In order to solve the high sampling rate issue in the multiuser UWB communication network system, the authors process the received signal with Bayesian compressive sensing. Using the characteristic that the wireless channels of multiuser signals which are received by one receiver at the same time are statistically related, a Laplace prior based distributed Bayesian compressive sensing method is proposed. It jointly reconstructs the received signals from different users and gets the parameters of channel models. The proposed method reduces the necessary sampling numbers for channel estimation. The experiment shows that the method improves the BER performance.
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