  Recently, deep learning has been proposed as a potential technique for
improving the physical layer performance of radio receivers. Despite the large
amount of encouraging results, most works have not considered spatial
multiplexing in the context of multiple-input and multiple-output (MIMO)
receivers. In this paper, we present a deep learning-based MIMO receiver
architecture that consists of a ResNet-based convolutional neural network, also
known as DeepRx, combined with a so-called transformation layer, all trained
together. We propose two novel alternatives for the transformation layer: a
maximal ratio combining-based transformation, or a fully learned
transformation. The former relies more on expert knowledge, while the latter
utilizes learned multiplicative layers. Both proposed transformation layers are
shown to clearly outperform the conventional baseline receiver, especially with
sparse pilot configurations. To the best of our knowledge, these are some of
the first results showing such high performance for a fully learned MIMO
receiver.
