Robotic Mobile System's Performance-Based MIMO-OFDM Technology

Authors

  • Omar Daoud Philadelphia University
  • Omar Alani University of Leeds

DOI:

https://doi.org/10.3991/ijim.v3i6.923

Keywords:

Neural Network, LDPC codes, Robot, MIMO, and OFDM

Abstract


In this paper, a predistortion neural network (PDNN) architecture has been imposed to the Sniffer Mobile Robot (SNFRbot) that is based on spatial multiplexed wireless Orthogonal Frequency Division Multiplexing (OFDM) transmission technology. This proposal is used to improve the system performance by combating one of the main drawbacks that is encountered by OFDM technology; Peak-to-Average Power Ratio (PAPR). Simulation results show that using PDNN resulted in better PAPR performance than the previously published work that is based on linear coding, such as Low Density Parity Check (LDPC) codes and turbo encoding whether using flat fading channel or a Doppler spread channel.

Author Biographies

Omar Daoud, Philadelphia University

ommunication and electronics department

Omar Alani, University of Leeds

School of Electronic and Electrical Engineering

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Published

2009-10-27

How to Cite

Daoud, O., & Alani, O. (2009). Robotic Mobile System’s Performance-Based MIMO-OFDM Technology. International Journal of Interactive Mobile Technologies (iJIM), 3(6), pp. 12–17. https://doi.org/10.3991/ijim.v3i6.923

Issue

Vol. 3 (2009): Special Issue: Technical Basics

Section

Special Focus Papers

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