A reduced complexity clustering algorithm for the K-user MIMO interference channel

In this paper, we propose a low complexity clustering algorithm for an overloaded MIMO interference channel. The algorithm is based on distributed interference alignment techniques. Starting from a small number of iterations in the iterative interference alignment process, the algorithm chooses the group of links that maximizes the chordal distance between the useful signal vector space and the interference signal vector space. Based on Monte-Carlo simulations, we evaluate the performance of the proposed algorithm in terms of system sum-capacity and compare it with the performance of optimal clustering and with the random clustering algorithms. The complexity of the proposed algorithm in terms of number of elementary operations is also studied.