Abstract
Interference alignment (IA) is an efficient interference management technique that can result in sum-rates that scale linearly with the number of users at high signal-to-noise power ratio (SNR). But it is difficult to directly apply IA to large networks because of heavy signaling overhead and feasibility constraint. Clustered IA divides cells into disjoint clusters where IA is applied in each cluster independently. It provides a mechanism for mitigating the signaling overhead and maximizing the achievable rate. In this paper, we devise a novel distributed coalition formation algorithm for cluster formation, which enables the cells to take individual decision on whether to cooperate or not, based on the pre-defined preference relation. Then we prove the convergence of the proposed algorithm: any initial cluster structure can finally converge to a stable solution of Nash equilibrium. Simulation results show that the proposed algorithm can get good performance.
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