Hardware Implementation of a MIMO Decoder Using Matrix Factorization Based Channel Estimation

Abstract This paper presents an efficient hardware realization of multiple-input multiple-output (MIMO) wireless communication decoder that utilizes the available resources by adopting the technique of parallelism. The hardware is designed and implemented on Xilinx Virtex™-4 XC4VLX60 field programmable gate arrays (FPGA) device in a modular approach which simplifies and eases hardware update, and facilitates testing of the various modules independently. The decoder involves a proficient channel estimation module that employs matrix factorization on least squares (LS) estimation to reduce a full rank matrix into a simpler form in order to eliminate matrix inversion. This results in performance improvement and complexity reduction of the MIMO system. Performance evaluation of the proposed method is validated through MATLAB simulations which indicate 2 dB improvement in terms of SNR compared to LS estimation. Moreover complexity comparison is performed in terms of mathematical operations, which shows that the proposed approach appreciably outperforms LS estimation at a lower complexity and represents a good solution for channel estimation technique.

[1]  Ahmed M. Eltawil,et al.  Design and Implementation of a Scalable Channel Emulator for Wideband MIMO Systems , 2009, IEEE Transactions on Vehicular Technology.

[2]  Yong Soo Cho,et al.  An FPGA Implementation of MML-DFE for Spatially Multiplexed MIMO Systems , 2006, IEEE Transactions on Circuits and Systems II: Express Briefs.

[3]  Siavash M. Alamouti,et al.  A simple transmit diversity technique for wireless communications , 1998, IEEE J. Sel. Areas Commun..

[4]  Angel Fernandez Herrero,et al.  Design and Implementation of a Hardware Module for MIMO Decoding in a 4G Wireless Receiver , 2008, VLSI Design.

[5]  Saeid Nooshabadi,et al.  Parametric minimum hardware QR-factoriser architecture for V-BLAST detection , 2006 .

[6]  Babak Daneshrad,et al.  Multi-antenna testbeds for research and education in wireless communications , 2004, IEEE Communications Magazine.

[7]  Robert Langwieser,et al.  Vienna MIMO Testbed , 2006, EURASIP J. Adv. Signal Process..

[8]  K. Kammeyer,et al.  Efficient algorithm for decoding layered space-time codes , 2001 .

[9]  A.B. Sesay,et al.  A recursive QR detector for space-frequency block coded OFDM systems with four transmit antennas , 2005, PACRIM. 2005 IEEE Pacific Rim Conference on Communications, Computers and signal Processing, 2005..

[10]  Lutz H.-J. Lampe,et al.  Multiple-antenna techniques for wireless communications - a comprehensive literature survey , 2009, IEEE Communications Surveys & Tutorials.

[11]  Luca Reggiani,et al.  Least Squares Channel Estimation for IEEE 802.16e Systems Based on Adaptive Taps Selection , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

[12]  Liuqing Yang,et al.  Optimal training for MIMO frequency-selective fading channels , 2005, IEEE Transactions on Wireless Communications.

[13]  Xiaohu You,et al.  Efficient MIMO channel estimation using complementary sequences , 2007, IET Commun..

[14]  Alex B. Gershman,et al.  Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals , 2006, IEEE Transactions on Signal Processing.

[15]  Gene H. Golub,et al.  Matrix computations , 1983 .

[16]  André Bourdoux,et al.  Implementation Aspects and Testbeds for MIMO Systems , 2006, EURASIP J. Adv. Signal Process..