Open Access Journal

ISSN : 2394 - 6849 (Online)

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Open Access Journal

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

ISSN : 2394-6849 (Online)

Complexity Reduction of MIMO Decoder

Author : Ch. Ashok Kumar Reddy 1 M. Anand 2

Date of Publication :7th August 2016

Abstract: The data rates and the supported range in communication systems, can be increased using MIMO (Multiple input and multiple output) technique. MIMO technique uses multiple antennas at both transmitter and receiver. MIMO systems uses Orthogonal frequency division multiplexing (OFDM) technique for multicarrier modulation. QR decomposition (QRD) is the first step in the decoding of the MIMO receiver. Gram Schmidt, Householder and Givens described QR decomposition method which are computationally intensive as these involve division operation for normalization. The computation complexity of these methods for MIMO-OFDM systems is difficult to handle because QR decomposition is performed for each subcarrier. Sphere decoder is an efficient decoder for MIMO systems. In this paper we use Modified Householder’s method for reducing the computation complexity without affecting the system packet error rate (PER) performance. The simulation process is carried out in all different models of 802.11 TGAC channels.

Reference :

  1. [1] Minjee KIM, “Performance comparision between Open Loop and Closed Loop MIMO-OFDM Schemes Using Analytical Approach”, IEEE Transactions on Communications, Vol.E95-B, Nov2012, pp.3498-3508.

    [2] L. G. Barbero and J. S. Thompson, “Performance analysis of a fixed complexity sphere decoder in high-dimensional mimo systems,” in Proc. IEEE ICASSP, vol. 4, May 2006, pp. 557-560.

    [3] G. Kalyana Krishnan and V. Umapathi Reddy, “High Performance Low Complexity Receiver for V-Blast,” IEEE 8th Workshop on Signal Processing in Wireless Communications, June 2007, pp. 1-5.

    [4] D. Wubben, J. Rinas, R. Bohnke, V. Kuhn, and K. D. Kammeyer, “Efficient algorithm for detecting layered spacetime codes,” in Proc. 4th Int. ITG Conf. Source and Channel Coding, Berlin, Germany, Jan 2002.

    [5] P. Luethi, C. Studer, S. Duetsch, E. Zgraggen, H. Kaeslin, N. Felber and W. Fichtner, “Gram-Schmidt Based QR Decomposition for MIMO Detection: VLSI Implementation and Comparision,” In Proceedings of the IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Macao, Chaina, November 2008.

    [6] K. L. Chung, W. M. Yan, “The Complex Householder Transform”, IEEE Transaction on Signal Processing, vol. 45, no.9, 2374-2376, Sep 1997.

    [7] G. H. Gollub and C. F. Van Loan, “Matrix Computations,” Baltimore MD, USA: John Hopkins University Press, 1996.

    [8] Y. T. Hwang, W. D. Chen, “A low complexity complex QR factorization design for signal detection in MIMO OFDM systems,” in Proc. IEEE ISCAS, May 2008, pp. 932-935.

    [9] N. K. Chavali and B. K. Kumar, "A reduced complexity MIMO decoder," Signal Processing, Informatics, Communication and Energy Systems (SPICES), 2015 IEEE International Conference on, Kozhikode, 2015, pp. 1-5.

    [10] TGac Channe Model Addendum Supporting Material, 2009. IEEE Standard. 802.11 – 09/0569r0, May 2009.


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