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

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

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)746-750
Number of pages5
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE100A
Issue number2
DOIs
Publication statusPublished - 2017 Feb 1

Fingerprint

Alignment
Interference
Clustering
Feedback
Clustering algorithms
Clustering Algorithm
Assignment
Scaling
Numerical Results
Resources

All Science Journal Classification (ASJC) codes

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

Cite this

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Feedback overhead-aware clustering for interference alignment in multiuser interference networks. / Min, Byoung Yoon; Kang, Heewon; Cho, Sungyoon; Jang, Jinyoung; Kim, Dong Ku.

In: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E100A, No. 2, 01.02.2017, p. 746-750.

Research output: Contribution to journalArticle

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