Channel estimation in UWB channels using compressed sensing

We consider sub-Nyquist sampling and Compressed Sensing (CS) for channel estimation in Ultra Wideband (UWB) communication systems, by exploiting the sparse nature of the channel impulse response. Receiver hardware schemes are presented that directly sample the analog channel output at rates far below Nyquist and allow access to a predefined subset of channel Fourier coefficients. CS methods are then applied to these coefficients in order to estimate the unknown channel. Simulations on Channel Model (CM) 1 of the IEEE 802.15.4a standard show that estimation is possible from low rate samples with little performance degradation paving the way to sub-Nyquist UWB channel sounding.

[1]  H. Nyquist,et al.  Certain Topics in Telegraph Transmission Theory , 1928, Transactions of the American Institute of Electrical Engineers.

[2]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[3]  Moe Z. Win,et al.  Ultra-wide bandwidth time-hopping spread-spectrum impulse radio for wireless multiple-access communications , 2000, IEEE Trans. Commun..

[4]  Thierry Blu,et al.  Sampling signals with finite rate of innovation , 2002, IEEE Trans. Signal Process..

[5]  Dong Ha,et al.  A frequency-domain approach for all-digital CMOS ultra wideband receivers , 2003, IEEE Conference on Ultra Wideband Systems and Technologies, 2003.

[6]  Anuj Batra,et al.  Multi-band OFDM Physical Layer Proposal , 2003 .

[7]  A. Molisch,et al.  IEEE 802.15.4a channel model-final report , 2004 .

[8]  Andreas F. Molisch,et al.  Ultrawideband propagation channels-theory, measurement, and modeling , 2005, IEEE Transactions on Vehicular Technology.

[9]  Harald Haas,et al.  Asilomar Conference on Signals, Systems, and Computers , 2006 .

[10]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[11]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[12]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

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

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

[15]  Hongjian Sun,et al.  Cooperative compressive spectrum sensing by sub-Nyquist sampling , 2009, 2009 First UK-India International Workshop on Cognitive Wireless Systems (UKIWCWS).

[16]  Brian M. Sadler,et al.  A Compressed Sensing Based Ultra-Wideband Communication System , 2009, 2009 IEEE International Conference on Communications.

[17]  Zhurui,et al.  Time and frequency domain receiver of UWB signal based on compressive , 2010 .

[18]  J. Lei,et al.  Time and frequency domain receiver of UWB signal based on compressive , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[19]  Eva Lagunas,et al.  Sparse channel estimation based on compressed sensing for ultra wideband systems , 2011, 2011 IEEE International Conference on Ultra-Wideband (ICUWB).

[20]  Hakan Ali Cirpan,et al.  The effect of channel models on compressed sensing based UWB channel estimation , 2011, 2011 IEEE International Conference on Ultra-Wideband (ICUWB).

[21]  Yonina C. Eldar,et al.  Multichannel Sampling of Pulse Streams at the Rate of Innovation , 2010, IEEE Transactions on Signal Processing.

[22]  Shuxu Guo,et al.  Compressed Sensing: Ultra-Wideband Channel Estimation Based on FIR Filtering Matrix , 2012 .

[23]  Yonina C. Eldar,et al.  Sub-Nyquist radar prototype: Hardware and algorithm , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[24]  Yonina C. Eldar.  Sampling Theory: Beyond Bandlimited Systems , 2015 .