A dynamic clustering algorithm based on NIR for interference alignment in ultra dense network

Ultra dense networks (UDN) are treated as a promising technology to meet the challenges of the future wireless communications where interference plays an important role in the network performance. Interference alignment (IA) has been considered to be a resultful technique for achieving the optimal capacity scaling. However, in practical communication system, mitigating all interference via IA requires heavy signaling overhead and high iteration complexity. In this paper, we propose a dynamic clustering algorithm based on graph partitioning with low complexity. Our work focuses on dividing the whole network into a number of clusters under size constraint and realizes the maximum intra-cluster interference and minimum inter-cluster interference. In addition, neighbor selecting scheme based on neighbor interference ratio (NIR) in proposed algorithm can get the proper cluster result in the random spatial network model. Furthermore, proposed algorithm is compared with other traditional algorithms in complexity and performance. The simulation results show that proposed algorithm reduces the complexity of clustering process significantly and achieves average 7% higher performance gain than existing clustering algorithms.