BER and NCMSE based Estimation algorithms for Underwater Noisy Channels

Fahad Khalil Paracha; Sheeraz Ahmed; M. Arshad Jaleel; Hamza Shahid; Umais Tayyab

Research Article

BER and NCMSE based Estimation algorithms for Underwater Noisy Channels

Download425 downloads

Cite: BibTeX Plain Text

@ARTICLE{10.4108/eai.26-3-2018.154380,
    author={Fahad Khalil Paracha and Sheeraz Ahmed and M. Arshad Jaleel and Hamza Shahid and Umais Tayyab},
    title={BER and NCMSE based Estimation algorithms for Underwater Noisy Channels},
    journal={EAI Endorsed Transactions on Internet of Things},
    volume={3},
    number={11},
    publisher={EAI},
    journal_a={IOT},
    year={2017},
    month={7},
    keywords={Least Square, Matching Pursuit, Least Mean Square, Normalized Channel Mean Square error, Bit error rate, Additive white gaussian noise},
    doi={10.4108/eai.26-3-2018.154380}
}

Fahad Khalil Paracha
Sheeraz Ahmed
M. Arshad Jaleel
Hamza Shahid
Umais Tayyab
Year: 2017
BER and NCMSE based Estimation algorithms for Underwater Noisy Channels
IOT
EAI
DOI: 10.4108/eai.26-3-2018.154380

Fahad Khalil Paracha¹^,*, Sheeraz Ahmed¹, M. Arshad Jaleel¹, Hamza Shahid², Umais Tayyab²

1: Department of Electrical Engineering, Gomal University, D.I.Khan, Pakistan.
2: Department of Electrical Engineering, King Fahd University of Petroleum and Mineral Sciences, Dhahran, Saudi Arabia

*Contact email: fkperacha@yahoo.com

Abstract

Channel estimation and equalization of sparse multipath channels is a real matter of concern for researchers in the recent past. Such type of channel impulse response is depicted by a very few significant non-zero taps that are widely separated in time. A comprehensive comparison of few algorithms in this regard has been provided. The algorithms simulated are LS, LMS and MP while simulation results along with observations are also presented in this paper. The metrics used for performance evaluation are Bit error rate (BER) and Normalized channel mean square error (NCMSE). On the basis of obtained simulation results, it is observed that MP algorithm requires shorter training sequence for estimation of channel response at the receiver as compared with LS. Furthermore, it is observed that MP has best performance while LS and LMS stand after respectively.

Keywords: Least Square, Matching Pursuit, Least Mean Square, Normalized Channel Mean Square error, Bit error rate, Additive white gaussian noise

Received: 2017-04-12
Accepted: 2017-06-06
Published: 2017-07-26
Publisher: EAI

: http://dx.doi.org/10.4108/eai.26-3-2018.154380

Copyright © 2017 Fahad Khalil Paracha et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.