Improved Proportionate Least Mean Square/Fourth Based Channel Equalization for Underwater Acoustic Communications

Ya-nan Tian; Xiao Han; Sergiy A. Vorobyov; Weizhe Li

doi:10.1109/CAMSAP58249.2023.10403523

Improved Proportionate Least Mean Square/Fourth Based Channel Equalization for Underwater Acoustic Communications

Ya-nan Tian, Xiao Han, Sergiy A. Vorobyov, Weizhe Li

Harbin Engineering University

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

9 Lataukset (Pure)

Abstrakti

An improved proportionate least mean square/fourth (IPLMS/F) equalizer is proposed in this paper, and applied to underwater acoustic communications in real experiment. In addition to improving the performance of least mean squares (LMS) equalizer, the proposed IPLMS/F equalizer maintains the simplicity and stability of LMS. The advantage of the proposed IPLMS/F equalizer is due to introduction of a proportional update matrix. The diagonal elements of this matrix are determined by the equalizer tap magnitudes to improve the sparsity level estimate, and thus, further improve the equalizer performance. The performance of IPLMS/F is verified by applying it to the experimental data from the 9^th Chinese National Arctic Research Expedition. The results show that IPLMS/F exhibits fastest convergence speed and it has the lowest bit error rate compared with LMS and LMS/F, indicating its effectiveness and reliability in practical applications.

Alkuperäiskieli	Englanti
Otsikko	2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023
Kustantaja	IEEE
Sivut	141-145
Sivumäärä	5
ISBN (elektroninen)	979-8-3503-4452-3
DOI - pysyväislinkit	https://doi.org/10.1109/CAMSAP58249.2023.10403523
Tila	Julkaistu - 2023
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Herradura, Costa Rica Kesto: 10 jouluk. 2023 → 13 jouluk. 2023 Konferenssinumero: 9

Julkaisusarja

Nimi	2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023

Workshop

Workshop	IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Lyhennettä	CAMSAP
Maa/Alue	Costa Rica
Kaupunki	Herradura
Ajanjakso	10/12/2023 → 13/12/2023

Pääsy asiakirjaan

10.1109/CAMSAP58249.2023.10403523

Improved Proportionate LMSF Channel Equalization For Underwater Acoustic CommunicationsAccepted author manuscript, 4,13 MB

Muut tiedostot ja linkit

Linkki julkaisuun Scopuksessa

Siteeraa tätä

Tian, Y., Han, X., Vorobyov, S. A., & Li, W. (2023). Improved Proportionate Least Mean Square/Fourth Based Channel Equalization for Underwater Acoustic Communications. teoksessa 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023 (Sivut 141-145). (2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023). IEEE. https://doi.org/10.1109/CAMSAP58249.2023.10403523

Tian, Ya-nan ; Han, Xiao ; Vorobyov, Sergiy A. et al. / Improved Proportionate Least Mean Square/Fourth Based Channel Equalization for Underwater Acoustic Communications. 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023. IEEE, 2023. Sivut 141-145 (2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023).

@inproceedings{a64cda0fb01d481a902a8e93adee12a1,

title = "Improved Proportionate Least Mean Square/Fourth Based Channel Equalization for Underwater Acoustic Communications",

abstract = "An improved proportionate least mean square/fourth (IPLMS/F) equalizer is proposed in this paper, and applied to underwater acoustic communications in real experiment. In addition to improving the performance of least mean squares (LMS) equalizer, the proposed IPLMS/F equalizer maintains the simplicity and stability of LMS. The advantage of the proposed IPLMS/F equalizer is due to introduction of a proportional update matrix. The diagonal elements of this matrix are determined by the equalizer tap magnitudes to improve the sparsity level estimate, and thus, further improve the equalizer performance. The performance of IPLMS/F is verified by applying it to the experimental data from the 9th Chinese National Arctic Research Expedition. The results show that IPLMS/F exhibits fastest convergence speed and it has the lowest bit error rate compared with LMS and LMS/F, indicating its effectiveness and reliability in practical applications.",

keywords = "proportional update matrix, sparse level, Underwater acoustic communication",

author = "Ya-nan Tian and Xiao Han and Vorobyov, {Sergiy A.} and Weizhe Li",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP ; Conference date: 10-12-2023 Through 13-12-2023",

year = "2023",

doi = "10.1109/CAMSAP58249.2023.10403523",

language = "English",

series = "2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023",

publisher = "IEEE",

pages = "141--145",

booktitle = "2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023",

address = "United States",

}

Tian, Y, Han, X, Vorobyov, SA & Li, W 2023, Improved Proportionate Least Mean Square/Fourth Based Channel Equalization for Underwater Acoustic Communications. julkaisussa 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023. 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023, IEEE, Sivut 141-145, IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Herradura, Costa Rica, 10/12/2023. https://doi.org/10.1109/CAMSAP58249.2023.10403523

Improved Proportionate Least Mean Square/Fourth Based Channel Equalization for Underwater Acoustic Communications. / Tian, Ya-nan; Han, Xiao; Vorobyov, Sergiy A. et al.
2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023. IEEE, 2023. s. 141-145 (2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Improved Proportionate Least Mean Square/Fourth Based Channel Equalization for Underwater Acoustic Communications

AU - Tian, Ya-nan

AU - Han, Xiao

AU - Vorobyov, Sergiy A.

AU - Li, Weizhe

N1 - Conference code: 9

PY - 2023

Y1 - 2023

N2 - An improved proportionate least mean square/fourth (IPLMS/F) equalizer is proposed in this paper, and applied to underwater acoustic communications in real experiment. In addition to improving the performance of least mean squares (LMS) equalizer, the proposed IPLMS/F equalizer maintains the simplicity and stability of LMS. The advantage of the proposed IPLMS/F equalizer is due to introduction of a proportional update matrix. The diagonal elements of this matrix are determined by the equalizer tap magnitudes to improve the sparsity level estimate, and thus, further improve the equalizer performance. The performance of IPLMS/F is verified by applying it to the experimental data from the 9th Chinese National Arctic Research Expedition. The results show that IPLMS/F exhibits fastest convergence speed and it has the lowest bit error rate compared with LMS and LMS/F, indicating its effectiveness and reliability in practical applications.

AB - An improved proportionate least mean square/fourth (IPLMS/F) equalizer is proposed in this paper, and applied to underwater acoustic communications in real experiment. In addition to improving the performance of least mean squares (LMS) equalizer, the proposed IPLMS/F equalizer maintains the simplicity and stability of LMS. The advantage of the proposed IPLMS/F equalizer is due to introduction of a proportional update matrix. The diagonal elements of this matrix are determined by the equalizer tap magnitudes to improve the sparsity level estimate, and thus, further improve the equalizer performance. The performance of IPLMS/F is verified by applying it to the experimental data from the 9th Chinese National Arctic Research Expedition. The results show that IPLMS/F exhibits fastest convergence speed and it has the lowest bit error rate compared with LMS and LMS/F, indicating its effectiveness and reliability in practical applications.

KW - proportional update matrix

KW - sparse level

KW - Underwater acoustic communication

UR - http://www.scopus.com/inward/record.url?scp=85184991405&partnerID=8YFLogxK

U2 - 10.1109/CAMSAP58249.2023.10403523

DO - 10.1109/CAMSAP58249.2023.10403523

M3 - Conference article in proceedings

AN - SCOPUS:85184991405

T3 - 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023

SP - 141

EP - 145

BT - 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023

PB - IEEE

T2 - IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing

Y2 - 10 December 2023 through 13 December 2023

ER -

Tian Y, Han X, Vorobyov SA, Li W. Improved Proportionate Least Mean Square/Fourth Based Channel Equalization for Underwater Acoustic Communications. julkaisussa 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023. IEEE. 2023. s. 141-145. (2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023). doi: 10.1109/CAMSAP58249.2023.10403523

Improved Proportionate Least Mean Square/Fourth Based Channel Equalization for Underwater Acoustic Communications

Abstrakti

Julkaisusarja

Workshop

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Siteeraa tätä