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- W2363711258 abstract "Ultra Wide-band(UWB) is a newly developing high-speed wireless communication technology.It is difficult to sample it directly as its wider bandwidth.However,compressed sensing(CS) provides a feasible way with lower sampling speed.Considering the current UWB channel estimation reconstruction algorithm which is based on CS must be used on the assumption that channel sparsity is known,an UWB channel estimation method based on bayesian compressed sensing(BCS) is proposed in this paper.UWB channel estimation is translated into CS reconstruction problem.Then BCS method is used in reconstruction to get the channel estimation value and error bound,finally decode UWB signal.BCS method introduces the sparse bayesian learning theory into CS.It sets posterior probability density function that is controlled by hyperparameters to each value in the vector that demand reconstruction.In the process of updating,many of the hyperparameters tend to infinity for those values that have insignificant amplitudes,and these corresponding posterior probability tends to zero.This method can reject trivial multipaths,find the critical paths in channel vector automatically and reconstruct them with regression algorithm.The experiment results show that this method can reconstruct original channel effectively under the circumstance that channel sparsity is unknown." @default.
- W2363711258 created "2016-06-24" @default.
- W2363711258 creator A5076565994 @default.
- W2363711258 date "2013-01-01" @default.
- W2363711258 modified "2023-09-28" @default.
- W2363711258 title "Ultra wide-band communication channel estimation based on bayesian compressed sensing" @default.
- W2363711258 hasPublicationYear "2013" @default.
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