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Recently, deep learning has been proposed as a potential technique forimproving the physical layer performance of radio receivers. Despite the largeamount of encouraging results, most works have not considered spatialmultiplexing in the context of multiple-input and multiple-output (MIMO)receivers. In this paper, we present a deep learning-based MIMO receiverarchitecture that consists of a ResNet-based convolutional neural network, alsoknown as DeepRx, combined with a so-called transformation layer, all trainedtogether. We propose two novel alternatives for the transformation layer: amaximal ratio combining-based transformation, or a fully learnedtransformation. The former relies more on expert knowledge, while the latterutilizes learned multiplicative layers. Both proposed transformation layers areshown to clearly outperform the conventional baseline receiver, especially withsparse pilot configurations. To the best of our knowledge, these are some ofthe first results showing such high performance for a fully learned MIMOreceiver.

DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations