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 | |
| Publication status | Published - 2017 Feb 1 |
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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 journal › Article
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 - 2017/2/1
Y1 - 2017/2/1
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 -