  Coordinated weighted sum-rate maximization in multicell MIMO networks with
intra- and intercell interference and local channel state at the base stations
is recognized as an important yet difficult problem. A classical, locally
optimal solution is obtained by the weighted minimum mean squared error (WMMSE)
algorithm which facilitates a distributed implementation in multicell networks.
However, it often suffers from slow convergence and therefore large
communication overhead. To obtain more practical solutions, the
unrolling/unfolding of traditional iterative algorithms gained significant
attention. In this work, we demonstrate a complete unfolding of the WMMSE
algorithm for transceiver design in multicell MU-MIMO interference channels
with local channel state information. The resulting architecture termed
GCN-WMMSE applies ideas from graph signal processing and is agnostic to
different wireless network topologies, while exhibiting a low number of
trainable parameters and high efficiency w.r.t. training data. It significantly
reduces the number of required iterations while achieving performance similar
to the WMMSE algorithm, alleviating the overhead in a distributed deployment.
Additionally, we review previous architectures based on unrolling the WMMSE
algorithm and compare them to GCN-WMMSE in their specific applicable domains.

Signal Processing