Compressed Sensing for Wireless Communications : A Few Tips and Tricks

As a paradigm to recover the sparse signal from a small set of linear measurements, compressed sensing (CS) has generated a great deal of interest in recent years. In order to apply the CS techniques to wireless communication systems, there are a number of things to consider. However, it is not easy to find simple and easy answers to those issues in research papers. The main purpose of this paper is to provide key premises and useful tips that wireless communication researchers need to know when designing CS-based wireless systems. These include promise and limitation of CS technique, subtle points that one should pay attention to, and discussion of wireless applications that CS technique can be applied to. The purpose of this paper is to provide essentials and useful tips that non-expert in the CS field needs to be aware of. Our hope is that this article will be a useful guide for wireless communication researchers to grasp the gist of CS

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

[2]  Jian Wang,et al.  Multipath Matching Pursuit , 2013, IEEE Transactions on Information Theory.

[3]  Yonina C. Eldar,et al.  Sub-Nyquist Sampling for Power Spectrum Sensing in Cognitive Radios: A Unified Approach , 2013, IEEE Transactions on Signal Processing.

[4]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[5]  Tareq Y. Al-Naffouri,et al.  Efficient Coordinated Recovery of Sparse Channels in Massive MIMO , 2014, IEEE Transactions on Signal Processing.

[6]  Bhaskar D. Rao,et al.  Channel estimation using joint dictionary learning in FDD massive MIMO systems , 2015, 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[7]  David B. Dunson,et al.  Multitask Compressive Sensing , 2009, IEEE Transactions on Signal Processing.

[8]  Namrata Vaswani,et al.  Modified-CS: Modifying compressive sensing for problems with partially known support , 2009, ISIT.

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

[10]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .

[11]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[12]  Bhaskar D. Rao,et al.  Sparse solutions to linear inverse problems with multiple measurement vectors , 2005, IEEE Transactions on Signal Processing.

[13]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[14]  Wotao Yin,et al.  Iteratively reweighted algorithms for compressive sensing , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[15]  Akbar M. Sayeed,et al.  Deconstructing multiantenna fading channels , 2002, IEEE Trans. Signal Process..

[16]  Bhaskar D. Rao,et al.  Sparse Signal Recovery With Temporally Correlated Source Vectors Using Sparse Bayesian Learning , 2011, IEEE Journal of Selected Topics in Signal Processing.

[17]  Shengli Zhou,et al.  Application of compressive sensing to sparse channel estimation , 2010, IEEE Communications Magazine.

[18]  Yonina C. Eldar,et al.  Average Case Analysis of Multichannel Sparse Recovery Using Convex Relaxation , 2009, IEEE Transactions on Information Theory.

[19]  Rachel Ward,et al.  Compressed Sensing With Cross Validation , 2008, IEEE Transactions on Information Theory.

[20]  Bhaskar D. Rao,et al.  Joint Approximately Sparse Channel Estimation and Data Detection in OFDM Systems Using Sparse Bayesian Learning , 2014, IEEE Transactions on Signal Processing.

[21]  Jun Won Choi,et al.  Downlink Pilot Reduction for Massive MIMO Systems via Compressed Sensing , 2015, IEEE Communications Letters.

[22]  Jun Won Choi,et al.  Statistical Recovery of Simultaneously Sparse Time-Varying Signals From Multiple Measurement Vectors , 2015, IEEE Transactions on Signal Processing.

[23]  Vincent K. N. Lau,et al.  Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems , 2014, IEEE Transactions on Signal Processing.

[24]  Bhaskar D. Rao,et al.  Joint dictionary learning and recovery algorithms in a jointly sparse framework , 2015, 2015 49th Asilomar Conference on Signals, Systems and Computers.

[25]  Pierre Vandergheynst,et al.  Compressed Sensing and Redundant Dictionaries , 2007, IEEE Transactions on Information Theory.

[26]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[27]  Yonina C. Eldar,et al.  Compressed Sensing with Coherent and Redundant Dictionaries , 2010, ArXiv.

[28]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[29]  Robert D. Nowak,et al.  Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels , 2010, Proceedings of the IEEE.

[30]  Stephen P. Boyd,et al.  Enhancing Sparsity by Reweighted ℓ1 Minimization , 2007, 0711.1612.

[31]  Bhaskar D. Rao,et al.  An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem , 2007, IEEE Transactions on Signal Processing.

[32]  R.G. Baraniuk,et al.  Distributed Compressed Sensing of Jointly Sparse Signals , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[33]  Jun Won Choi,et al.  New approach for massive MIMO detection using sparse error recovery , 2014, 2014 IEEE Global Communications Conference.

[34]  Joel A. Tropp,et al.  Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit , 2006, Signal Process..

[35]  Bhaskar D. Rao,et al.  Sparse channel estimation via matching pursuit with application to equalization , 2002, IEEE Trans. Commun..

[36]  Joseph F. Murray,et al.  Dictionary Learning Algorithms for Sparse Representation , 2003, Neural Computation.

[37]  Philip Schniter,et al.  Efficient High-Dimensional Inference in the Multiple Measurement Vector Problem , 2011, IEEE Transactions on Signal Processing.

[38]  Shengli Zhou,et al.  Sparse channel estimation for multicarrier underwater acoustic communication: From subspace methods to compressed sensing , 2009, OCEANS 2009-EUROPE.

[39]  Lenan Wu,et al.  A Study of Deterministic Pilot Allocation for Sparse Channel Estimation in OFDM Systems , 2012, IEEE Communications Letters.

[40]  Robert W. Heath,et al.  Spatially Sparse Precoding in Millimeter Wave MIMO Systems , 2013, IEEE Transactions on Wireless Communications.

[41]  Bhaskar D. Rao,et al.  Sparse Bayesian learning for basis selection , 2004, IEEE Transactions on Signal Processing.

[42]  J. Preisig,et al.  Estimation of Rapidly Time-Varying Sparse Channels , 2007, IEEE Journal of Oceanic Engineering.

[43]  Bhaskar D. Rao,et al.  Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm , 1997, IEEE Trans. Signal Process..