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 |
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Graphics and Computer-Aided Design
- Electrical and Electronic Engineering
- Applied Mathematics
Fingerprint Dive into the research topics of 'Feedback overhead-aware clustering for interference alignment in multiuser interference networks'. Together they form a unique fingerprint.
Cite this
Min, B. Y., Kang, H., Cho, S., Jang, J., & Kim, D. K. (2017). Feedback overhead-aware clustering for interference alignment in multiuser interference networks. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E100A(2), 746-750. https://doi.org/10.1587/transfun.E100.A.746