Noncoherent detection based on compressed sensing for ultra-wideband impulse radio

Noncoherent ultra-wideband (UWB) receivers are suboptimal but have advantage in low-complexity and low-power consumption. The theory of compressed sensing (CS) enables the reconstruction or approximation of sparse or compressible signals from a small set of incoherent projections. This paper presents a noncoherent detection approach based on CS for pulse ultra-wideband systems. The Matching Pursuit (MP) algorithm with implicit denoising operation is used as the signal reconstruction method of CS for pulse UWB. The dictionary of the MP algorithm is weighted with the prior knowledge of the average power delay profile (APDP) of the channel to achieve higher performance. Simulations show that the proposed noncoherent detection approach is resilient to additive noise and outperforms the conventional energy detection method.

[1]  W. Hirt,et al.  ML receiver for pulsed UWB signals and partial channel state information , 2005, 2005 IEEE International Conference on Ultra-Wideband.

[2]  G.R. Arce,et al.  Ultra-Wideband Compressed Sensing: Channel Estimation , 2007, IEEE Journal of Selected Topics in Signal Processing.

[3]  Xu Ma,et al.  Compressed Detection for Pilot Assisted Ultra-Wideband Impulse Radio , 2007, 2007 IEEE International Conference on Ultra-Wideband.

[4]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[5]  Andreas F. Molisch,et al.  Channel models for ultrawideband personal area networks , 2003, IEEE Wireless Communications.

[6]  J. Romme,et al.  Noncoherent ultra-wideband systems , 2009, IEEE Signal Processing Magazine.

[7]  G.R. Arce,et al.  Compressed detection for ultra-wideband impulse radio , 2007, 2007 IEEE 8th Workshop on Signal Processing Advances in Wireless Communications.

[8]  Richard G. Baraniuk,et al.  Sparse Signal Detection from Incoherent Projections , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[9]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[10]  Richard G. Baraniuk,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.