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.

In this paper, hierarchical interference coordination is proposed that suppresses both intra- and inter-cluster interference (ICI) in clustered wireless networks. Assuming transmitters and receivers are equipped with multiple antennas and complete channel state information is shared among all transmitters within the same cluster, interference alignment (IA) is performed that uses nulls to suppress intra-cluster interference. For ICI mitigation, we propose a null-steering precoder designed on the nullspace of a principal eigenvector of the correlated ICI channels, which eliminates a significant amount of ICI power given the exchange of cluster geometry between neighboring clusters. However, as ICI is negligible for the system in which the distance between clusters are large enough, the proposed scheme may not improve the system performance compared with the pure IA scheme that exploits all spatial degrees of freedom (DoF) to increase multiplexing gain without ICI mitigation. For the efficient interference management between intra- and inter-cluster, we analyze the decision criterion that provides an adaptive transmission mode selection between pure IA and proposed ICI reduction in given network environments. Moreover, a low computational complexity based transmission mode switching algorithm is proposed for irregularly distributed networks.