TY - GEN
T1 - Frequency channel equalization based on variable step-size LMS algorithm for OFDM underwater communications
AU - Sui, Zeping
AU - Yan, Shefeng
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - In this paper, we intend to improve the performance of the least mean square (LMS) channel equalization in terms of bit error rate (BER) and mean square error (MSE) for orthogonal frequency division multiplexing (OFDM) underwater acoustic (UWA) communications by establishing a nonlinear function relationship between the step-size and the error signal. By ignoring the intersymbol interference (ISI), we propose to consider the frequency LMS channel equalizer in OFDM communication systems as a combination of many parallel single tap frequency domain subequalizers, and thus each subcarrier is individually equalized. The proposed method improves the equalization performance sufficiently in comparison with the existing ones by using large step-sizes initially to improve the convergence rate and small step-sizes that change slowly to match with small error signals. Simulation results demonstrate the superiority of the proposed method.
AB - In this paper, we intend to improve the performance of the least mean square (LMS) channel equalization in terms of bit error rate (BER) and mean square error (MSE) for orthogonal frequency division multiplexing (OFDM) underwater acoustic (UWA) communications by establishing a nonlinear function relationship between the step-size and the error signal. By ignoring the intersymbol interference (ISI), we propose to consider the frequency LMS channel equalizer in OFDM communication systems as a combination of many parallel single tap frequency domain subequalizers, and thus each subcarrier is individually equalized. The proposed method improves the equalization performance sufficiently in comparison with the existing ones by using large step-sizes initially to improve the convergence rate and small step-sizes that change slowly to match with small error signals. Simulation results demonstrate the superiority of the proposed method.
KW - Adaptive channel equalization
KW - Least mean square
KW - Orthogonal frequency division multiplexing
KW - Underwater acoustic communication
UR - http://www.scopus.com/inward/record.url?scp=85078899747&partnerID=8YFLogxK
U2 - 10.1109/ICSPCC46631.2019.8960813
DO - 10.1109/ICSPCC46631.2019.8960813
M3 - Conference contribution
AN - SCOPUS:85078899747
T3 - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
BT - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
Y2 - 20 September 2019 through 22 September 2019
ER -
