FPGA Implementation of Sphere Detector for Spatial Multiplexing MIMO System

Authors

  • Asmaa Mohamed Kamel ASU
  • Abdel halim Zekry ASU
  • Reem Ibrahim Sayed

Abstract

Multiple Input Multiple Output (MIMO (techniquesuse multiple antennas at both transmitter and receiver forincreasing the channel reliability and enhancing the spectralefficiency of wireless communication system.MIMO Spatial Multiplexing (SM) is a technology that can increase the channelcapacity without additional spectral resources. The implementation of MIMO detection techniques become a difficult missionas the computational complexity increases with the number of
transmitting antenna and constellation size. So designing detection techniques that can recover transmitted signals from SpatialMultiplexing (SM) MIMO with reduced complexity and highperformance is challenging. In this survey, the general model ofMIMO communication system is presented in addition to multipleMIMO Spatial Multiplexing (SM) detection techniques. These detection techniques are divided into different categories, such as linear detection, Non-linear detection and tree-search detection.
Detailed discussions on the advantages and disadvantages of each detection algorithm are introduced. Hardware implementation of Sphere Decoder (SD) algorithm using VHDL/FPGA is also
presented

Author Biographies

Asmaa Mohamed Kamel, ASU

Telecommunication Engineering

Abdel halim Zekry, ASU

Telecommunication Engineering

Reem Ibrahim Sayed

Telecommunication Engineering

References

JeffreyG.Andrews, ArunabhaGhosh, and Rais Mohamed,

“Fundamentals of WiMAX: Understanding Broadband Wireless Networking”, Prentice Hall, 2007.

JanMietzner, RobertSchober, Lutz Lampe, Wolfgang H.Gerstacker, Peter A. Hoeher, “Multiple-Antenna Techniques for Wireless Communications – A Comprehensive iterature Survey,” IEEE communications survey and tutorials, Vol II

No 2, Second quarter 2009.

G. J. Foschini, “Layered space-time architecture for wireless communication in a fading environment when using multielement antennas,” Bell Syst. Tech. J., pp. 41–59, Autumn1996.

G. J. Foschini and M. J. Gans, “On limits of wireless

communications in a fading environment when using multiple

antennas,” Kluwer Wireless Pers. Commun., vol. 6, pp. 311–

, Mar. 1998

W. Van Etten, “Maximum likelihood receiver for multiple

channel transmission systems,” IEEE Trans. on Communications, pp. 276-283, Feb. 1976.

Z. Xu and R. D. Murch, “Performance analysis of maximum likelihood detectionin a MIMO antenna system,” IEEE

Trans. Commun., vol. 50, no. 2, pp. 187– 191, Feb. 2002

P. Wolniansky, G. Foschini, G. Golden, and R. Valenzuela,

“V-BLAST: An architecture for realizing very high data rates

over the rich-scattering wireless channel,” in Proc. URSI International Symposium on Signals, Systems, and Electronics,

pp. 295-300, Oct.

J. Paulraj, D. A. Gore, R. U. Nabar, and H. Boelcskei,

“Anoverview of MIMO communications – A key to gigabit

wireless,” Proc. IEEE, vol. 92, no. 2, pp. 198–218, Feb. 2004.

P. W. Wolniansky, G. J. Foschini, G. D. Golden, and R. A.

Valenzuela, “VBLAST: An architecture for realizing very high

data rates over the rich-scattering wireless channel,” URSI Int.

Symp. on Signals, Systems and Electronics, (Pisa, Italy), pp.

, Sep. 1998

D. Shiu and J.M. Kahn, nLayered Space-Time Codes for

Wireless Communications using Multiple TransmitAntennas,”

in IEEE Proceedings of International Conference on Communications (ICC’99), Vancouver, B.C.,June 6-10 1999

D. W ¨ ubben, J. Rinas, R. B ¨ ohnke, V. K ¨ uhn, and K.D.

Kammeyer, “Efficient Algorithm for Detecting Layered SpaceTime Codes”, 4th Int. ITG Conference on Source and Channel Coding, Berlin, January 2002

U. Finke, and M. Pohst, “Improved methods for calculating vectors of short length in a lattice, including a complexity

analysis,” Mathematics of Computation, vol.44, no. 170, pp.

-471, April 1985.

E. Viterbo and E. Bigleri, “A universal lattice decoder,”

In 14eme Colloque GRETSI, pp. 611-614, September 1993.

E. Viterbo and J. Boutros, “A universal lattice code

decoder for fading channels,” IEEE Trans. Inform. Theory,

vol. 45, no. 5, pp. 1639–1642, July 1999.

M. O. Damen, H. E. Gamal, and G. Caire, “On maximum

likelihood detection and the search for the closest lattice point,” IEEE Trans. Inform. Theory, vol. 49, no.10, pp. 2389–

, Oct. 2003.

B. Hassibi and H. Vikalo, “On Sphere Decoding algorithm. Part I,the expected complexity,”IEEE Transactions on

Signal Processing,vol. 54, no. 5, pp. 2806–2818, August 2005.

Z. Guo and P. Nilsson, “Algorithm and implementation

of the K-BestSphereDecoding for MIMO Detection,”IEEE

Journal on SelectedAreas in Communications, vol. 24, no. 3,

pp. 491–503, March 2006.

M. O. Damen, H. E. Gamal, and G. Caire, “On MaximumLikelihood detection and the search for the closest lattice

point,”IEEE Transactions on Information Theory, vol. 49, no.

, pp. 2389–2402, October2003.

K. Su, “Efficient Maximum Likelihood detection for communicationover MIMO channels,” University of Cambridge,

Technical Report,February 2005.

C. P. Schnorr and M. Euchner, “Lattice basis reduction:

Improved practical algorithms and solving subset sum problems,” Math. Program., vol. 66,pp. 181–191,1994.

E. Agrell, T. Eriksson, A. Vardy, and K. Zegar, “Closet

point search in lattices,”IEEE Trans. Inf. Theory, vol. 48, pp.

–2214, Aug. 2002.

A. D. Murugan, H. E. Gamal, M. O. Damen, and G. Caire,

“A unified frameworkfortree search decoding: rediscovering

the sequential decoder,” IEEE Trans. Inf.Theory, vol. 52, no.

, pp. 933–953, Mar. 2006

B. Hassibi and H. Vikalo, “On Sphere Decoding algorithm. Part I,the expected complexity,”IEEE Transactions on Signal Processing,vol. 54, no. 5, pp. 2806–2818, August 2005

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Published

2024-04-19

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Section

Telecommunications