Deep HyperNetwork-Based MIMO Detection
This addresses the challenge of practical and efficient MIMO detection for wireless communication systems, offering a solution that balances performance and computational demands, though it is incremental as it builds on existing neural network approaches.
The paper tackles the NP-hard problem of optimal symbol detection for MIMO systems by proposing a hypernetwork that generates weights for a neural network-based detector based on the channel matrix, achieving near state-of-the-art performance without requiring retraining for each channel realization.
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.