Deep Unfolding in Multicell MU-MIMO

Abstract

The weighted sum-rate maximization in coordinated multicell MIMO networks with intra- and intercell interference and local channel state at the base stations is considered. Based on the concept of unrolling applied to the classical weighted minimum mean squared error (WMMSE) algorithm and ideas from graph signal processing, we present the GCN-WMMSE deep network architecture for transceiver design in multicell MU-MIMO interference channels with local channel state information. Similar to the original WMMSE algorithm it facilitates a distributed implementation in multicell networks. However, GCN-WMMSE significantly accelerates the convergence and con-sequently alleviates the communication overhead in a distributed deployment. Additionally, the architecture is agnostic to different wireless network topologies while exhibiting a low number of trainable parameters and high efficiency w.r.t. training data.