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

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.