Group sparsity methods for compressive channel estimation in doubly dispersive multicarrier systems

We propose advanced compressive estimators of doubly dispersive channels within multicarrier communication systems (including classical OFDM systems). The performance of compressive channel estimation has been shown to be limited by leakage components impairing the channel's effective delay-Doppler sparsity. We demonstrate a group sparse structure of these leakage components and apply recently proposed recovery techniques for group sparse signals. We also present a basis optimization method for enhancing group sparsity. Statistical knowledge about the channel can be incorporated in the basis optimization if available. The proposed estimators outperform existing compressive estimators with respect to estimation accuracy and, in one instance, also computational complexity.

[1]  M. Rudelson,et al.  Sparse reconstruction by convex relaxation: Fourier and Gaussian measurements , 2006, 2006 40th Annual Conference on Information Sciences and Systems.

[2]  Patrick Flandrin,et al.  Time-Frequency/Time-Scale Analysis , 1998 .

[3]  Akbar M. Sayeed,et al.  Capacity of Sparse Multipath Channels in the Ultra-Wideband Regime , 2007, IEEE Journal of Selected Topics in Signal Processing.

[4]  R. Nowak,et al.  Learning sparse doubly-selective channels , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[5]  Yonina C. Eldar,et al.  Coherence-Based Performance Guarantees for Estimating a Sparse Vector Under Random Noise , 2009, IEEE Transactions on Signal Processing.

[6]  Mark W. Schmidt,et al.  GROUP SPARSITY VIA LINEAR-TIME PROJECTION , 2008 .

[7]  R. Nowak,et al.  Compressed sensing of wireless channels in time, frequency, and space , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.

[8]  Jun Jason Zhang,et al.  Compressive sensing and waveform design for the identification of Linear time-varying systems , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  P. Bello Characterization of Randomly Time-Variant Linear Channels , 1963 .

[10]  Holger Rauhut,et al.  Compressive Estimation of Doubly Selective Channels in Multicarrier Systems: Leakage Effects and Sparsity-Enhancing Processing , 2009, IEEE Journal of Selected Topics in Signal Processing.

[11]  Manfred Martin Hartmann,et al.  Analysis, Optimization, and Implementation of Low-Interference Wireless Multicarrier Systems , 2007, IEEE Transactions on Wireless Communications.

[12]  Holger Rauhut,et al.  Multichannel-compressive estimation of doubly selective channels in MIMO-OFDM systems: Exploiting and enhancing joint sparsity , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[13]  Anna Scaglione,et al.  Application of sparse signal recovery to pilot-assisted channel estimation , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[14]  Yonina C. Eldar,et al.  Block-Sparse Signals: Uncertainty Relations and Efficient Recovery , 2009, IEEE Transactions on Signal Processing.

[15]  Yonina C. Eldar,et al.  Robust Recovery of Signals From a Structured Union of Subspaces , 2008, IEEE Transactions on Information Theory.

[16]  Lajos Hanzo,et al.  Multiuser MIMO-OFDM for Next-Generation Wireless Systems , 2007, Proceedings of the IEEE.

[17]  Andreas F. Molisch,et al.  Nonorthogonal pulseshapes for multicarrier communications in doubly dispersive channels , 1998, IEEE J. Sel. Areas Commun..

[18]  Volkan Cevher,et al.  Model-Based Compressive Sensing , 2008, IEEE Transactions on Information Theory.

[19]  Joel A. Tropp,et al.  Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.

[20]  Franz Hlawatsch,et al.  A compressed sensing technique for OFDM channel estimation in mobile environments: Exploiting channel sparsity for reducing pilots , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.