Reduction of Inter-Symbol Interference using Artificial Neural Network System in Multicarrier OFDM System

Authors

  • Jyoti Makkar  M.Tech Scholar, ECE Department, J.C.D.M College of Engineering, Sirsa, Haryana, India
  • Dr. Himanshu Monga  Professor, ECE Department, J.C.D.M College of Engineering, Sirsa Haryana, India
  • Silki Baglha  Assistant Professor, ECE Department, J.C.D.M College of Engineering, Sirsa Haryana, India

Keywords:

OFDM, Artificial Neural Network (ANN), FFT, QAM, BER, ISI, MMSE

Abstract

The work proposes Inter-Symbol Interference (ISI) reduction scheme, ISI is a major problem in Optical systems, which produces various types of non-linear distortions. So the implementation of OFDM system using Artificial Neural Network (ANN) scheme with M-QAM modulation technique is proposed and compared with the conventional OFDM system without using ANN. This proposed scheme is an implementation of Back-propagation (BP) algorithm over AWGN channels to achieve an effective ISI reduction in orthogonal frequency division multiplexing (OFDM) systems. Simulation results prove that ANN equalizer can further reduce ISI effectively and provide acceptable BER and better MSE plot compared to the conventional OFDM system.

References

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Published

2017-10-31

Issue

Section

Research Articles

How to Cite

[1]
Jyoti Makkar, Dr. Himanshu Monga, Silki Baglha, " Reduction of Inter-Symbol Interference using Artificial Neural Network System in Multicarrier OFDM System, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.553-558, September-October-2017.