Offline Learning Approach for Deep-Unfolded MIMO Detector via Vector Similarity Search

This paper proposes a vector similarity search (VSS) based offline learning approach for the deep-unfolded multiple-input multiple-output (MIMO) detector. The VSS offline learning approach consists of an offline learning phase and a real-time detection phase. In the offline learning phase, trained parameters of the deep-unfolded MIMO detector are stored in a vector database with a feature vector extracted from the channel matrix. In the real-time detection phase, the detector parameters are retrieved from the database with similarity matching of the feature vector. The critical advantage of the proposal is that it can offload the training computational cost from the edge server to the training server. Numerical results indicate that the VSS offline learning provides appropriate convergence acceleration in almost all cases, and that it improves the robustness of the deep-unfolded MIMO detector in dynamic channel environments.