Robust decentralized joint precoding using team deep neural network

Using Deep Neural Networks (DNNs) to tackle socalled Team Decision problems where the nodes aim at maximizing an expected common utility on the basis of different individual observations without any additional communications was recently introduced in a previous work and illustrated in the simple case of decentralized scheduling. In this work1, we apply this idea to design a decentralized robust precoding scheme in a Network MIMO configuration, which appears as a more challenging setting due to the continuous decision space and the required fine granularity of the precoding, in particular at high

SNR. While the application remains fundamentally decentralized due to the decentralized nature of the channel state information (CSI), the training is done jointly. This is possible thanks to the common knowledge of the statistics (or equivalently the training data set) at all cooperating TXs. The joint training is done directly with respect to the desired figure-of-merit such that there is no need to generate labels using another method, and the precoding scheme obtained from the training does not only replicate a known scheme but is able to outperform state-of-the-art methods, as illustrated by simulations.