• Jun 03, 2019 News!Vol.9, No.5-Vol.10, No.3 have been indexed by EI (Inspec).   [Click]
  • Sep 28, 2021 News!Vol.13, No.4 has been published with online version.   [Click]
  • Jun 18, 2021 News!Vol.13, No.3 has been published with online version.   [Click]
General Information
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2010 Vol.2(6): 842-850 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2010.V2.250

Hybridization of ANN and GA with Adaptive Mutation: A Proficient Technique for Optimal Transmit Antenna Subset Selection for MIMO-OFDM Systems

Madan Lal* and Dr Ashok De**

Abstract—The integration of Orthogonal Frequency Division Multiplexing (OFDM) technique along with Multiple Input Multiple Output (MIMO) systems seems to be under focus and also serves as a challenging research in the field of broadband wireless communication during topical dates. The resulting MIMO-OFDM system maneuvers the following high data rate wireless transmission of OFDM as well as maximized system capacity of MIMO. Even with these advantages, a foremost issue that influence the MIMO-OFDM system is the hardware perplexity aroused due to the mounted quantity of transmits and receives antennae. In prior works, a technique on the basis of Genetic Algorithm (GA) along with adaptive mutation is being proposed; still, the technique undergoes computational complexity. To surmount these disadvantages, in this paper, a hybrid technique is proposed to choose the optimal transmit antenna subset. The proposed hybrid technique is developed by blending of Artificial Neural Network (ANN) and GA with adaptive mutation. In the work, a training set is generated using GA with adaptive mutation and the generated training set is used to train the ANN using Back Propagation (BP) algorithm. The well-trained ANN selects the optimal transmit antennas when the required number of antennas to be selected and the desired Signal to Noise Ratio (SNR) level are given. The implementation results show that the proposed hybrid technique effectively selects the optimal transmit antennas with good ergodic capacity and less computational complexity.

Index Terms—MIMO-OFDM, transmit antenna selection, ANN, GA with adaptive mutation, ergodic capacity, SNR, BP.

*Corresponding Author : Madan Lal, Professor and Head, Department of Electronics and Communication Engineering, Bhai Gurdas Institute of Engineering and Technology, Sangrur -148001, Punjab, India. (e-mail: madansharma.20@gmail.com).
**Prof.(Dr.) Ashok De, Principal, Ambedkar Institute of Technology, Government of Delhi, Delhi, India


Cite: Madan Lal and Dr Ashok De, "Hybridization of ANN and GA with Adaptive Mutation: A Proficient Technique for Optimal Transmit Antenna Subset Selection for MIMO-OFDM Systems," International Journal of Computer Theory and Engineering vol. 2, no. 6, pp. 842-850, 2010.  

Copyright © 2008-2021. International Association of Computer Science and Information Technology. All rights reserved.