TL;DR
This paper proposes a distributed coalition formation approach for long-term base station clustering in cellular networks, optimizing interference mitigation and throughput with low complexity and minimal CSI overhead.
Contribution
It introduces a novel long-term throughput model and a distributed coalition formation algorithm for scalable, interference-aware base station clustering.
Findings
Achieves within 10% of the global optimal long-term throughput.
Develops a low-complexity, distributed coalition formation algorithm.
Provides a robust short-term precoding method considering intercoalition interference.
Abstract
Interference alignment (IA) is a promising technique for interference mitigation in multicell networks due to its ability to completely cancel the intercell interference through linear precoding and receive filtering. In small networks, the amount of required channel state information (CSI) is modest and IA is therefore typically applied jointly over all base stations. In large networks, where the channel coherence time is short in comparison to the time needed to obtain the required CSI, base station clustering must be applied however. We model such clustered multicell networks as a set of coalitions, where CSI acquisition and IA precoding is performed independently within each coalition. We develop a long-term throughput model which includes both CSI acquisition overhead and the level of interference mitigation ability as a function of the coalition structure. Given the throughput…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
