  Coordinated precoding based on interference alignment is a promising
technique for improving the throughputs in future wireless multicell networks.
In small networks, all base stations can typically jointly coordinate their
precoding. In large networks however, base station clustering is necessary due
to the otherwise overwhelmingly high channel state information (CSI)
acquisition overhead. In this work, we provide a branch and bound algorithm for
finding the globally optimal base station clustering. The algorithm is mainly
intended for benchmarking existing suboptimal clustering schemes. We propose a
general model for the user throughputs, which only depends on the long-term CSI
statistics. The model assumes intracluster interference alignment and is able
to account for the CSI acquisition overhead. By enumerating a search tree using
a best-first search and pruning sub-trees in which the optimal solution
provably cannot be, the proposed method converges to the optimal solution. The
pruning is done using specifically derived bounds, which exploit some assumed
structure in the throughput model. It is empirically shown that the proposed
method has an average complexity which is orders of magnitude lower than that
of exhaustive search.
