IoT 18(11): e3

Research Article

BER and NCMSE based Estimation algorithms for Underwater Noisy Channels

Download214 downloads
  • @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={2018},
        month={3},
        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: 2018
    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.