  Base station clustering is necessary in large interference networks, where
the channel state information (CSI) acquisition overhead otherwise would be
overwhelming. In this paper, we propose a novel long-term throughput model for
the clustered users which addresses the balance between interference mitigation
capability and CSI acquisition overhead. The model only depends on statistical
CSI, thus enabling long-term clustering. Based on notions from coalitional game
theory, we propose a low-complexity distributed clustering method. The
algorithm converges in a couple of iterations, and only requires limited
communication between base stations. Numerical simulations show the viability
of the proposed approach.
