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

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Summary :

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.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.2 pp.746-750

Publication Date: 2017/02/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E100.A.746

Type of Manuscript: LETTER

Category: Communication Theory and Signals

Authors

Byoung-Yoon MIN
  Yonsei University
Heewon KANG
  Yonsei University
Sungyoon CHO
  Yonsei University
Jinyoung JANG
  Yonsei University
Dong Ku KIM
  Yonsei University

Keyword

limited feedback, clustering, interference alignment

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Byoung-Yoon MIN, Heewon KANG, Sungyoon CHO, Jinyoung JANG, Dong Ku KIM, "Feedback Overhead-Aware Clustering for Interference Alignment in Multiuser Interference Networks" in IEICE TRANSACTIONS on Fundamentals, vol. E100-A, no. 2, pp. 746-750, February 2017, doi: 10.1587/transfun.E100.A.746.
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.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.746/_p

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@ARTICLE{e100-a_2_746,
author={Byoung-Yoon MIN, Heewon KANG, Sungyoon CHO, Jinyoung JANG, Dong Ku KIM, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Feedback Overhead-Aware Clustering for Interference Alignment in Multiuser Interference Networks},
year={2017},
volume={E100-A},
number={2},
pages={746-750},
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.},
keywords={},
doi={10.1587/transfun.E100.A.746},
ISSN={1745-1337},
month={February},}

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TY - JOUR
TI - Feedback Overhead-Aware Clustering for Interference Alignment in Multiuser Interference Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 746
EP - 750
AU - Byoung-Yoon MIN
AU - Heewon KANG
AU - Sungyoon CHO
AU - Jinyoung JANG
AU - Dong Ku KIM
PY - 2017
DO - 10.1587/transfun.E100.A.746
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2017
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.
ER -