Feedback overhead-aware clustering for interference alignment in multiuser interference networks

Byoung Yoon Min; Heewon Kang; Sungyoon Cho; Jinyoung Jang; Dong Ku Kim

doi:10.1587/transfun.E100.A.746

Feedback overhead-aware clustering for interference alignment in multiuser interference networks

Byoung Yoon Min, Heewon Kang, Sungyoon Cho, Jinyoung Jang, Dong Ku Kim

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

Interference alignment (IA) is a promising technology for eliminating interferences while it still achieves the optimal capacity scaling. However, in practical systems, the IA feasibility limit and the heavy signaling overhead obstructs employing IA to large-scale networks. In order to jointly consider these issues, we propose the feedback overhead-aware IA clustering algorithm which comprises two parts: Adaptive feedback resource assignment and dynamic IA clustering. Numerical results show that the proposed algorithm offers significant performance gains in comparison with conventional approaches.

Original language	English
Pages (from-to)	746-750
Number of pages	5
Journal	IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Volume	E100A
Issue number	2
DOIs	https://doi.org/10.1587/transfun.E100.A.746
Publication status	Published - 2017 Feb

Bibliographical note

Publisher Copyright:
© 2017 The Institute of Electronics, Information and Communication Engineers.

All Science Journal Classification (ASJC) codes

Signal Processing
Computer Graphics and Computer-Aided Design
Electrical and Electronic Engineering
Applied Mathematics

Access to Document

10.1587/transfun.E100.A.746

Cite this

@article{4dbfab7452b84ae6b6a0d7446daf9cc8,

title = "Feedback overhead-aware clustering for interference alignment in multiuser interference networks",

abstract = "Interference alignment (IA) is a promising technology for eliminating interferences while it still achieves the optimal capacity scaling. However, in practical systems, the IA feasibility limit and the heavy signaling overhead obstructs employing IA to large-scale networks. In order to jointly consider these issues, we propose the feedback overhead-aware IA clustering algorithm which comprises two parts: Adaptive feedback resource assignment and dynamic IA clustering. Numerical results show that the proposed algorithm offers significant performance gains in comparison with conventional approaches.",

author = "Min, {Byoung Yoon} and Heewon Kang and Sungyoon Cho and Jinyoung Jang and Kim, {Dong Ku}",

note = "Publisher Copyright: {\textcopyright} 2017 The Institute of Electronics, Information and Communication Engineers.",

year = "2017",

month = feb,

doi = "10.1587/transfun.E100.A.746",

language = "English",

volume = "E100A",

pages = "746--750",

journal = "IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences",

issn = "0916-8508",

publisher = "Maruzen Co., Ltd/Maruzen Kabushikikaisha",

number = "2",

}

TY - JOUR

T1 - Feedback overhead-aware clustering for interference alignment in multiuser interference networks

AU - Min, Byoung Yoon

AU - Kang, Heewon

AU - Cho, Sungyoon

AU - Jang, Jinyoung

AU - Kim, Dong Ku

PY - 2017/2

Y1 - 2017/2

N2 - Interference alignment (IA) is a promising technology for eliminating interferences while it still achieves the optimal capacity scaling. However, in practical systems, the IA feasibility limit and the heavy signaling overhead obstructs employing IA to large-scale networks. In order to jointly consider these issues, we propose the feedback overhead-aware IA clustering algorithm which comprises two parts: Adaptive feedback resource assignment and dynamic IA clustering. Numerical results show that the proposed algorithm offers significant performance gains in comparison with conventional approaches.

AB - Interference alignment (IA) is a promising technology for eliminating interferences while it still achieves the optimal capacity scaling. However, in practical systems, the IA feasibility limit and the heavy signaling overhead obstructs employing IA to large-scale networks. In order to jointly consider these issues, we propose the feedback overhead-aware IA clustering algorithm which comprises two parts: Adaptive feedback resource assignment and dynamic IA clustering. Numerical results show that the proposed algorithm offers significant performance gains in comparison with conventional approaches.

UR - http://www.scopus.com/inward/record.url?scp=85011876483&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85011876483&partnerID=8YFLogxK

U2 - 10.1587/transfun.E100.A.746

DO - 10.1587/transfun.E100.A.746

M3 - Article

AN - SCOPUS:85011876483

VL - E100A

SP - 746

EP - 750

JO - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

JF - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

SN - 0916-8508

IS - 2

ER -

Feedback overhead-aware clustering for interference alignment in multiuser interference networks

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this