  Interference alignment (IA) is a promising technique for interference
mitigation in multicell networks due to its ability to completely cancel the
intercell interference through linear precoding and receive filtering. In small
networks, the amount of required channel state information (CSI) is modest and
IA is therefore typically applied jointly over all base stations. In large
networks, where the channel coherence time is short in comparison to the time
needed to obtain the required CSI, base station clustering must be applied
however. We model such clustered multicell networks as a set of coalitions,
where CSI acquisition and IA precoding is performed independently within each
coalition. We develop a long-term throughput model which includes both CSI
acquisition overhead and the level of interference mitigation ability as a
function of the coalition structure. Given the throughput model, we formulate a
coalitional game where the involved base stations are the rational players.
Allowing for individual deviations by the players, we formulate a distributed
coalition formation algorithm with low complexity and low communication
overhead that leads to an individually stable coalition structure. The dynamic
clustering is performed using only long-term CSI, but we also provide a robust
short-term precoding algorithm which accounts for the intercoalition
interference when spectrum sharing is applied between coalitions. Numerical
simulations show that the distributed coalition formation is generally able to
reach long-term sum throughputs within 10 % of the global optimum.
