Compressed SNR-and-channel estimation for beam tracking in 60-GHz WLAN

Signal-to-noise ratio (SNR) and channel estimations are critical for 60-GHz communications to track the optimal transmission and reception beam pairs. However, the excessive pilot overhead for the estimations severely reduces system throughput in fast-rotation scenarios. In order to address this problem, we firstly demonstrate the potential sparseness property of 60-GHz channel in beam tracking; subsequently, via exploiting this property, we propose a novel compressed SNR-and-channel estimation. The estimation is conducted in a three-stage fashion, including the unstructured estimation, nonzero-tap detection, and structured estimation with nonzero-tap location. Numerical simulations show that, in the case of substantial reduction of the pilot overhead, the proposed estimator still reveals a significant improvement in terms of estimation performance over the scheme in IEEE 802.11ad. Furthermore, it is also demonstrated that the proposed SNR and channel estimators can approach the lower bounds in sparse channels so long as SNR exceeds 8 dB.

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

[2]  Chiu Ngo,et al.  Supporting Uncompressed HD Video Streaming without Retransmissions over 60GHz Wireless Networks , 2008, 2008 IEEE Wireless Communications and Networking Conference.

[3]  Alberto Valdes-Garcia,et al.  60GHz Technology for Gbps WLAN and WPAN: From Theory to Practice , 2010 .

[4]  Yong-Kweon Kim,et al.  V-band 2-b and 4-b low-loss and low-voltage distributed MEMS digital phase shifter using metal-air-metal capacitors , 2002, IMS 2002.

[5]  Yang Wen,et al.  CAZAC sequence and its application in LTE random access , 2006, 2006 IEEE Information Theory Workshop - ITW '06 Chengdu.

[6]  Yuan-Hao Huang,et al.  SNR Estimation Based on Metric Normalization Frequency in Viterbi Decoder , 2011, IEEE Communications Letters.

[7]  Chin-Sean Sum,et al.  Golay sequence aided channel estimation for millimeter-wave WPAN systems , 2008, 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications.

[8]  P. Vainikainen,et al.  Statistical Channel Models for 60 GHz Radio Propagation in Hospital Environments , 2012, IEEE Transactions on Antennas and Propagation.

[9]  Ada S. Y. Poon,et al.  Coding the Beams: Improving Beamforming Training in mmWave Communication System , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[10]  Khawza I. Ahmed,et al.  Superimposed training-based compressed sensing of sparse multipath channels , 2012, IET Commun..

[11]  Theodore S. Rappaport,et al.  A 38 GHz cellular outage study for an urban outdoor campus environment , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[12]  Chin-Sean Sum,et al.  Single carrier transmission for multi-gigabit 60-GHz WPAN systems , 2009, IEEE Journal on Selected Areas in Communications.

[13]  Robert D. Nowak,et al.  Compressed channel sensing , 2008, 2008 42nd Annual Conference on Information Sciences and Systems.

[14]  Feng Wan,et al.  Semi-Blind Most Significant Tap Detection for Sparse Channel Estimation of OFDM Systems , 2010, IEEE Transactions on Circuits and Systems I: Regular Papers.

[15]  Chin-Sean Sum,et al.  Beam Codebook Based Beamforming Protocol for Multi-Gbps Millimeter-Wave WPAN Systems , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[16]  Urbashi Mitra,et al.  Sparse Channel Estimation with Zero Tap Detection , 2007, IEEE Transactions on Wireless Communications.

[17]  A. Maltsev,et al.  Statistical channel model for 60 GHz WLAN systems in conference room environment , 2010, Proceedings of the Fourth European Conference on Antennas and Propagation.

[18]  Jing Gao,et al.  Beam codebook based beamforming protocol for multi-Gbps millimeter-wave WPAN systems , 2009, IEEE Journal on Selected Areas in Communications.

[19]  Urbashi Mitra,et al.  Sparse channel estimation with zero tap detection , 2004, 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577).

[20]  Zhang,et al.  A Novel UWB Signal Sampling Method for Localization based on Compressive Sensing , 2010 .

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

[22]  Ada S. Y. Poon,et al.  Detecting Human Blockage and Device Movement in mmWave Communication System , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[23]  T. Kurner,et al.  Analyzing human body shadowing at 60 GHz: Systematic wideband MIMO measurements and modeling approaches , 2012, 2012 6th European Conference on Antennas and Propagation (EUCAP).