The BOX-LASSO with application to GSSK modulation in massive MIMO systems

The BOX-LASSO is a variant of the popular LASSO that includes an additional box-constraint. We propose its use as a decoder in modern Multiple Input Multiple Output (MIMO) communication systems with modulation methods such as the Generalized Space Shift Keying (GSSK) modulation, which produces constellation vectors that are inherently sparse and with bounded elements. In that direction, we prove novel explicit asymptotic characterizations of the squared-error and of the per-element error rate of the BOX-LASSO, under iid Gaussian measurements. In particular, the theoretical predictions can be used to quantify the improved performance of the BOX-LASSO, when compared to the previously used standard LASSO. We include simulation results that validate both these premises and our theoretical predictions.

[1]  Christos Thrampoulidis,et al.  LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements , 2015, NIPS.

[2]  Alfred O. Hero,et al.  Sparse Image Reconstruction for Molecular Imaging , 2008, IEEE Transactions on Image Processing.

[3]  Mihailo Stojnic,et al.  A framework to characterize performance of LASSO algorithms , 2013, ArXiv.

[4]  Ali Ghrayeb,et al.  Generalized space shift keying modulation for MIMO channels , 2008, 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications.

[5]  Mihailo Stojnic,et al.  Recovery thresholds for ℓ1 optimization in binary compressed sensing , 2010, 2010 IEEE International Symposium on Information Theory.

[6]  W. Newey,et al.  Large sample estimation and hypothesis testing , 1986 .

[7]  Andrea Montanari,et al.  Universality in polytope phase transitions and iterative algorithms , 2012, 2012 IEEE International Symposium on Information Theory Proceedings.

[8]  Harald Haas,et al.  Generalised spatial modulation , 2010, 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers.

[9]  Andrea Montanari,et al.  The LASSO Risk for Gaussian Matrices , 2010, IEEE Transactions on Information Theory.

[10]  Andrea Montanari,et al.  Message-passing algorithms for compressed sensing , 2009, Proceedings of the National Academy of Sciences.

[11]  Christos Thrampoulidis,et al.  Regularized Linear Regression: A Precise Analysis of the Estimation Error , 2015, COLT.

[12]  Wenlong Liu,et al.  Denoising Detection for the Generalized Spatial Modulation System Using Sparse Property , 2014, IEEE Communications Letters.

[13]  Mohamed-Slim Alouini,et al.  Digital Communication over Fading Channels: Simon/Digital Communications 2e , 2004 .

[14]  Andrea Montanari,et al.  Applications of the Lindeberg Principle in Communications and Statistical Learning , 2010, IEEE Transactions on Information Theory.

[15]  Christos Thrampoulidis,et al.  Ber analysis of the box relaxation for BPSK signal recovery , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[16]  Christos Thrampoulidis,et al.  Precise Error Analysis of Regularized $M$ -Estimators in High Dimensions , 2016, IEEE Transactions on Information Theory.

[17]  Joel A. Tropp,et al.  Universality laws for randomized dimension reduction, with applications , 2015, ArXiv.

[18]  Andrea Montanari,et al.  The dynamics of message passing on dense graphs, with applications to compressed sensing , 2010, ISIT.

[19]  Soo-Chang Pei,et al.  Compressed Sensing Detector Design for Space Shift Keying in MIMO Systems , 2012, IEEE Communications Letters.