  Massive multiple-input multiple-output (MIMO) is a key technology for
emerging next-generation wireless systems. Utilizing large antenna arrays at
base-stations, massive MIMO enables substantial spatial multiplexing gains by
simultaneously serving a large number of users. However, the complexity in
massive MIMO signal processing (e.g., data detection) increases rapidly with
the number of users, making conventional hand-engineered algorithms less
computationally efficient. Low-complexity massive MIMO detection algorithms,
especially those inspired or aided by deep learning, have emerged as a
promising solution. While there exist many MIMO detection algorithms, the aim
of this magazine paper is to provide insight into how to leverage deep neural
networks (DNN) for massive MIMO detection. We review recent developments in
DNN-based MIMO detection that incorporate the domain knowledge of established
MIMO detection algorithms with the learning capability of DNNs. We then present
a comparison of the key numerical performance metrics of these works. We
conclude by describing future research areas and applications of DNNs in
massive MIMO receivers.
