Compressive sensing for ultra-wideband channel estimation: on the sparsity assumption of ultra-wideband channels

2013 ◽  
pp. n/a-n/a ◽  
Author(s):  
Mehmet Başaran ◽  
Serhat Erküçük ◽  
Hakan Ali Çırpan
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Ultra Wideband ◽  
Wideband Channels ◽  
Wideband Channel

A New Way of Ultra-Wideband Channel Estimation Based on Bayesian Compressive Sensing

2012 ◽  
Vol 591-593 ◽  
pp. 1334-1337
Author(s):  
Fen Lan Li ◽  
Hua Wen ◽  
Zhe Min Zhuang
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Probability Model ◽  
Sampling Rate ◽  
Bp Algorithm ◽  
Least Square ◽  
Ultra Wideband ◽  
A Posteriori ◽  
The Time Domain ◽  
Uwb Channel

In this paper, in order to solve the problem that the sampling rate in ultra-wideband (UWB) channel estimation is too high, we discuss the applicability of Bayesian Compressive Sensing (BCS) used in UWB channel estimation. We solve the problem by using the time domain sparse of the impulse response of the UWB channel and establishing the probability model of the Compressive Sensing (CS) measurement. We accomplish the channel estimation by optimizing maximum a posteriori (MAP) of the channel. The simulation results show that the proposed scheme needs a very low sampling rate to recover the channel accurately. And the BCS algorithm has a better performance than the basis pursuit (BP) algorithm and the traditional least square (LS) algorithm in bit error rate (BER).

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