A New Design Framework for Sparse FIR MIMO Equalizers

In this paper, we propose a new framework for the design of sparse finite impulse response (FIR) equalizers. We start by formulating greedy and convex-optimization-based solutions for sparse FIR linear equalizer tap vectors given a maximum allowable loss in the decision-point signal-to-noise ratio. Then, we extend our formulation to decision feedback equalizers and multiple-antenna systems. This is followed by further generalization to the channel shortening setup which is important for communication systems operating over broadband channels with long channel impulse responses. We propose a novel approach to design a sparse target impulse response. Finally, as an application of current practical interest, we consider self far-end crosstalk cancellation on vectored very high-speed digital subscriber line systems for cellular backhaul networks.

[1]  S. Chia,et al.  The next challenge for cellular networks: backhaul , 2009, IEEE Microwave Magazine.

[2]  V. Koivunen,et al.  Sparse equalization in high data rate WCDMA systems , 2007, 2007 IEEE 8th Workshop on Signal Processing Advances in Wireless Communications.

[3]  Christina Fragouli,et al.  How to choose the number of taps in a DFE , 2002 .

[4]  Naofal Al-Dhahir FIR channel-shortening equalizers for MIMO ISI channels , 2001, IEEE Trans. Commun..

[5]  Tolga M. Duman,et al.  Error Rate Improvement in Underwater MIMO Communications Using Sparse Partial Response Equalization , 2006 .

[6]  E. Masry,et al.  Chip-Level DS-CDMA Downlink Interference Suppression With Optimized Finger Placement , 2006, IEEE Transactions on Signal Processing.

[7]  Shlomo Shamai,et al.  On the capacity of a twisted-wire pair: Gaussian model , 1990, IEEE Trans. Commun..

[8]  Gideon Kutz,et al.  Determination of Tap Positions for Sparse Equalizers , 2007, IEEE Transactions on Communications.

[9]  N. Al-Dhahir,et al.  Efficiently computed reduced-parameter input-aided MMSE equalizers for ML detection: a unified approach , 1996, IEEE Trans. Inf. Theory.

[10]  P. Duvaut,et al.  Construction of a DSL-MIMO channel model for evaluation of FEXT cancellation systems in VDSL2 , 2007, 2007 IEEE Sarnoff Symposium.

[11]  Naofal Al-Dhahir,et al.  Sparse FIR Equalization: A New Design Framework , 2011, 2011 IEEE Vehicular Technology Conference (VTC Fall).

[12]  Thomas A. Baran,et al.  Linear Programming Algorithms for Sparse Filter Design , 2010, IEEE Transactions on Signal Processing.

[13]  Naofal Al-Dhahir,et al.  Low-Complexity Sparse FIR Channel Shortening , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[14]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[15]  Zhifeng Zhang,et al.  Adaptive time-frequency decompositions , 1994 .

[16]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.

[17]  John M. Cioffi,et al.  Very-high-speed digital subscriber lines , 1999, IEEE Commun. Mag..

[18]  Ali H. Sayed,et al.  The finite-length multi-input multi-output MMSE-DFE , 2000, IEEE Trans. Signal Process..

[19]  Marc Moonen,et al.  Partial Crosstalk Cancellation for Upstream VDSL , 2004, EURASIP J. Adv. Signal Process..

[20]  Nevio Benvenuto,et al.  The Viterbi algorithm for sparse channels , 1996, IEEE Trans. Commun..

[21]  J. Cioffi,et al.  MMSE Decision-Feedback Equalizers: , 1995 .

[22]  L. Greenstein,et al.  Tap-selectable decision-feedback equalization , 1997, IEEE Trans. Commun..

[23]  Sheng Chen,et al.  Orthogonal least squares methods and their application to non-linear system identification , 1989 .

[24]  John M. Cioffi,et al.  Vectored transmission for digital subscriber line systems , 2002, IEEE J. Sel. Areas Commun..

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