A Mathematical modeling and Simulation Analysis of Lattice reduction algorithms for large MIMO detections


(*) Corresponding author


Authors' affiliations


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


Abstract – This paper analyses the performance of Multiple Input Multiple Output (MIMO) wireless communication system by combining it with an efficient algorithm Diagonal Reduction (DR). DR is a powerful tool to achieve more efficiently a high performance with less complexity. This paper proposes a  DR algorithm in order to reduce its complexity named Greedy Diagonal Reduction (GDR) algorithm which gives reduced complexity with efficient performance at the receiver using the MATLAB communication tool box version 7.0 as a simulation tool.

From the simulation results of various reduction algorithms it is observed that,

  • DR  can reduce the number of iterations using size reduction operations. Proposed DR algorithm  gives identical   Bit Error Rate (BER) performance with  LLL algorithms when applied to Successive Interference cancellation (SIC) decoding.
  • Greedy DR reduces the computational complexity in Multiple Input Multiple Output systems by improving the efficiency   in terms of   size reduction operations.
  • Sphere Decoding (SD) technique gives better BER performance on comparing with Successive Interference cancellation decoding technique.

Copyright © 2014 Praise Worthy Prize - All rights reserved.

Keywords


Multiple Input Multiple Output (MIMO), Greedy Diagonal Reduction (GDR) algorithm, Diagonal Reduction (DR).Zero Forcing, Sphere Decoding, Successive interference cancellation (SIC), LLL algorithm.

Full Text:

PDF


References


REFERENCES

C. Windpassinger, L. Lampe, R. F. H. Fischer, and T. Hehn, “A

performance study of MIMO detectors,”IEEE Trans. Wireless

Commun., vol. 5, no. 8, pp. 2004–2008, Aug. 2006.

A. K. Lenstra, H. W. Lenstra, and L. Lovasz, “Factoring poly-

nomials with rational coefficients,”Math. Ann., vol. 261, no. 4,

pp. 515–534, Dec. 1982.

X. Ma and W. Zhang, “Performance analysis for MIMO sys-

tems with lattice-reduction aided linear equalization,”

IEEE Trans. Commun. , vol. 56, no. 2, pp. 309–318, Feb. 2008.

P. Silvola, K. Hooli, and M. Juntti, “Suboptimal soft-output

MAP detector with lattice reduction,”IEEE Signal Process.

Lett., vol. 13, no. 6, pp. 321–324, Jun. 2006.

T.aherzadeh,, M., Mobasher,, A., and Khandani

, A. K., “LLL Lattice-Basis Reduction Achieves Maximum Diversity In MIMO Systems,” in IEEE International Symposium

on Information Theory, ISIT’05 , (Adelaide, Australia), Sept. 2005.

S teingrimsson , B., Luo, T., and Wong, K. M., “Soft quasi-maximum-likelihood detection for multiple-antenna wireless channels,”IEEE Transactions on Signal Processing., vol. 51, pp. 2710– 2719, Nov. 2003.


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize