Partial parallel interference cancellation multiuser detection using recurrent neural network based on Hebb learning rule

Li Yanping; Zhang Yongbo; Wang Huakui

doi:10.1109/WCICA.2006.1712914

Partial parallel interference cancellation multiuser detection using recurrent neural network based on Hebb learning rule

Li Yanping^*
, Zhang Yongbo
, Wang Huakui

^*Corresponding author for this work

Taiyuan University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In CDMA communication systems, in order to decrease the influence on reception performance resulted from incorrect decision of the interference users' information bits in parallel interference cancellation (PIC) process, a recurrent neural network based on Hebb learning rule is designed and applied to adjusting interference cancellation factors (ICF) in partial parallel interference cancellation (PPIC) multiuser detection. Simulation results show that the proposed Hebb-PPIC detection has strong anti-MAI ability and its performance of bit error rate (BER) is improved on the basis of conventional PIC in both conditions of ideal power control and "near-far" scenario.

Original language	English
Title of host publication	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Pages	2989-2992
Number of pages	4
DOIs	https://doi.org/10.1109/WCICA.2006.1712914
State	Published - 2006
Externally published	Yes
Event	6th World Congress on Intelligent Control and Automation, WCICA 2006 - Dalian, China Duration: 21 Jun 2006 → 23 Jun 2006

Publication series

Name	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Volume	1

Conference

Conference	6th World Congress on Intelligent Control and Automation, WCICA 2006
Country/Territory	China
City	Dalian
Period	21/06/06 → 23/06/06

Keywords

Hebb learning rule
Multiuser detection
Partial parallel interference cancellation (PPIC)
Recurrent neural network

Access to Document

10.1109/WCICA.2006.1712914

Cite this

Yanping, L., Yongbo, Z., & Huakui, W. (2006). Partial parallel interference cancellation multiuser detection using recurrent neural network based on Hebb learning rule. In Proceedings of the World Congress on Intelligent Control and Automation (WCICA) (pp. 2989-2992). Article 1712914 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 1). https://doi.org/10.1109/WCICA.2006.1712914

@inproceedings{45c2003c074148b384686ca02127d112,

title = "Partial parallel interference cancellation multiuser detection using recurrent neural network based on Hebb learning rule",

abstract = "In CDMA communication systems, in order to decrease the influence on reception performance resulted from incorrect decision of the interference users' information bits in parallel interference cancellation (PIC) process, a recurrent neural network based on Hebb learning rule is designed and applied to adjusting interference cancellation factors (ICF) in partial parallel interference cancellation (PPIC) multiuser detection. Simulation results show that the proposed Hebb-PPIC detection has strong anti-MAI ability and its performance of bit error rate (BER) is improved on the basis of conventional PIC in both conditions of ideal power control and {"}near-far{"} scenario.",

keywords = "Hebb learning rule, Multiuser detection, Partial parallel interference cancellation (PPIC), Recurrent neural network",

author = "Li Yanping and Zhang Yongbo and Wang Huakui",

year = "2006",

doi = "10.1109/WCICA.2006.1712914",

language = "英语",

isbn = "1424403324",

series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

pages = "2989--2992",

booktitle = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

note = "6th World Congress on Intelligent Control and Automation, WCICA 2006 ; Conference date: 21-06-2006 Through 23-06-2006",

}

Yanping, L, Yongbo, Z & Huakui, W 2006, Partial parallel interference cancellation multiuser detection using recurrent neural network based on Hebb learning rule. in Proceedings of the World Congress on Intelligent Control and Automation (WCICA)., 1712914, Proceedings of the World Congress on Intelligent Control and Automation (WCICA), vol. 1, pp. 2989-2992, 6th World Congress on Intelligent Control and Automation, WCICA 2006, Dalian, China, 21/06/06. https://doi.org/10.1109/WCICA.2006.1712914

Partial parallel interference cancellation multiuser detection using recurrent neural network based on Hebb learning rule. / Yanping, Li; Yongbo, Zhang; Huakui, Wang.
Proceedings of the World Congress on Intelligent Control and Automation (WCICA). 2006. p. 2989-2992 1712914 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Yanping, Li

AU - Yongbo, Zhang

AU - Huakui, Wang

PY - 2006

Y1 - 2006

N2 - In CDMA communication systems, in order to decrease the influence on reception performance resulted from incorrect decision of the interference users' information bits in parallel interference cancellation (PIC) process, a recurrent neural network based on Hebb learning rule is designed and applied to adjusting interference cancellation factors (ICF) in partial parallel interference cancellation (PPIC) multiuser detection. Simulation results show that the proposed Hebb-PPIC detection has strong anti-MAI ability and its performance of bit error rate (BER) is improved on the basis of conventional PIC in both conditions of ideal power control and "near-far" scenario.

AB - In CDMA communication systems, in order to decrease the influence on reception performance resulted from incorrect decision of the interference users' information bits in parallel interference cancellation (PIC) process, a recurrent neural network based on Hebb learning rule is designed and applied to adjusting interference cancellation factors (ICF) in partial parallel interference cancellation (PPIC) multiuser detection. Simulation results show that the proposed Hebb-PPIC detection has strong anti-MAI ability and its performance of bit error rate (BER) is improved on the basis of conventional PIC in both conditions of ideal power control and "near-far" scenario.

KW - Hebb learning rule

KW - Multiuser detection

KW - Partial parallel interference cancellation (PPIC)

KW - Recurrent neural network

UR - https://www.scopus.com/pages/publications/34047237612

U2 - 10.1109/WCICA.2006.1712914

DO - 10.1109/WCICA.2006.1712914

M3 - 会议稿件

AN - SCOPUS:34047237612

SN - 1424403324

SN - 9781424403325

T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

SP - 2989

EP - 2992

BT - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

T2 - 6th World Congress on Intelligent Control and Automation, WCICA 2006

Y2 - 21 June 2006 through 23 June 2006

ER -

Yanping L, Yongbo Z, Huakui W. Partial parallel interference cancellation multiuser detection using recurrent neural network based on Hebb learning rule. In Proceedings of the World Congress on Intelligent Control and Automation (WCICA). 2006. p. 2989-2992. 1712914. (Proceedings of the World Congress on Intelligent Control and Automation (WCICA)). doi: 10.1109/WCICA.2006.1712914

Partial parallel interference cancellation multiuser detection using recurrent neural network based on Hebb learning rule

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Keywords

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