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Impact of Different Training Mode on Adaptive Equalization Techniques for MIMO-OFDM System

Bashar A. Mohammed, Siddeeq Y. Ameen. Published in Signal Processing.

Communications on Applied Electronics
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Bashar A. Mohammed, Siddeeq Y. Ameen

Bashar A Mohammed and Siddeeq Y Ameen. Impact of Different Training Mode on Adaptive Equalization Techniques for MIMO-OFDM System. Communications on Applied Electronics 7(2):29-33, May 2017. BibTeX

	author = {Bashar A. Mohammed and Siddeeq Y. Ameen},
	title = {Impact of Different Training Mode on Adaptive Equalization Techniques for MIMO-OFDM System},
	journal = {Communications on Applied Electronics},
	issue_date = {May 2017},
	volume = {7},
	number = {2},
	month = {May},
	year = {2017},
	issn = {2394-4714},
	pages = {29-33},
	numpages = {5},
	url = {},
	doi = {10.5120/cae2017652603},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The paper investigates the performance enhancement of MIMO-OFDM system by using LMS, VSSLMS, SignLMS and RLS adaptive equalizers. Precisely the paper compares between two methods of training mode in equalizers that are used with MIMO-OFDM system, the Full Frame (FF) method that uses one frame from sets of frames as a desired signal and the Part Frame (PF) method uses part of the frame as a desired signal. The investigation aims to determine which method of training is best among the adopted equalizers in terms of tolerance to AWGN, adjustment speed and complexity. This has been achieved via computer simulation of the four equalization techniques mentioned earlier under the two forms of training modes. The results of the investigation show that the FF mode of training is preferable with LMS, VSSLMS and SignLMS and can be resumed every 1/16 frames. However, the PF is preferable when the RLS is used and can be resumed every 1/32 of the frame size.


  1. Popovski, P. et al 2013, Novel radio link concepts and state of the art analysis, Petra Weitkemper (DOCOMO Euro-Labs).
  2. Saleem, S. 2012 Optimization of Channel Estimation Algorithms for MIMO-OFDM based LTE-Advanced, IJCSI, Volume 2.
  3. Kansal, B. L. 2012 Performance Analysis of MIMO-OFDM by Spatial Diversity with STBC4, International Journal of Computer Applications Volume 48– No.20, 2012.
  4. Malik, G. and Sappal, A. S. 2011 Adaptive Equalization Algorithms:An Overview. (IJACSA) Vol. 2, No.3.
  5. Mahmood, L., Shirazi, S. F., Naz, S., Shirazi, S. H., Razzak, M. I., Umar, A I., and Ashra, S. S. 2015 Adaptive Filtering Algorithms for Channel Equalization in Wireless Communication, Indian Journal of Science and Technology, Vol 8(17).
  6. Ahmed, M. A, Jimaa S. A., and Abualhaol, I. Y. 2012 Performance Enhancements of MIMO-OFDM System Using Various Adaptive Receiver Structures, International Journal of Computer and Information Technology (2277 – 0764) Volume 01– Issue 01.
  7. Jagan V.; Murali K.; Krishna K. Rajeswari, R. 2011 Performance Analysis of Equalization Techniques for MIMO Systems in Wireless Communication, International Journal of Smart Home Vol.4, No.4.
  8. Farhang-Boroujeny B. 2013 Adaptive filters: theory and applications, 2nd Edition, Wiley.
  9. Kim J. S et al. 2015 An Adaptive Equalizer for High-Speed Receiver using a CDR-Assisted All-Digital Jitter Measurement“, Journal of Semiconductor Technology and Science, Vol.15, No.2.


MIMO-OFDM, Channel Equalization, LMS, VSSLMS, RLS