Deep HyperNetwork-Based MIMO Detection

Mathieu Goutay (1, 2) , Faycal Ait Aoudia (1) , Jakob Hoydis (1)

1 Nokia Bell Labs [Nozay]
2 MARACAS - Modèle et algorithmes pour des systèmes de communication fiables

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. Index Terms—MIMO Detection, Deep Learning, Hypernetworks, spatial channel correlation

Domaines: Théorie de l'information [cs.IT]; Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]

Mathieu Goutay, Faycal Ait Aoudia, Jakob Hoydis. Deep HyperNetwork-Based MIMO Detection. SPAWC 2020 - IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications, May 2020, Atlanta, United States. pp.1-5, ⟨10.1109/SPAWC48557.2020.9154283⟩. ⟨hal-03393742⟩