  Optimal symbol detection for multiple-input multiple-output (MIMO) systems is
known to be an NP-hard problem. Conventional heuristic algorithms are either
too complex to be practical or suffer from poor performance. Recently, several
approaches tried to address those challenges by implementing the detector as a
deep neural network. However, they either still achieve unsatisfying
performance on practical spatially correlated channels, or are computationally
demanding since they require retraining for each channel realization. In this
work, we address both issues by training an additional neural network (NN),
referred to as the hypernetwork, which takes as input the channel matrix and
generates the weights of the neural NN-based detector. Results show that the
proposed approach achieves near state-of-the-art performance without the need
for re-training.
