Application of Bayesian compressive sensing in IRUWB channel estimation

Due to the sparse nature of the impulse radio ultra-wideband (IR-UWB) communication channel in the time domain, compressive sensing (CS) theory is very suitable for the sparse channel estimation. Besides the sparse nature, the IR-UWB channel has shown more features which can be taken into account in the channel estimation process, such as the clustering structures. In this paper, by taking advantage of the clustering features of the channel, a novel IR-UWB channel estimation scheme based on the Bayesian compressive sensing (BCS) framework is proposed, in which the sparse degree of the channel impulse response is not required. Extensive simulation results show that the proposed channel estimation scheme has obvious advantages over the traditional scheme, and the final demodulation performance, in terms of Bit Error Rate (BER), is therefore greatly improved.

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