In the near-field region of an extremely large-scale MIMO (XL MIMO) system, channel reconstruction can be solved by utilizing sparse parameter estimation after transforming the received pilots at the base station (BS) into Cartesian domain. However, the process of exhaustive search over the codebook consumes significant computational resources and running time, especially when dealing with a vast number of antennas. In this study, we visualize the sparse channel matrix in the Cartesian domain as an channel image and propose a deep neural network, i.e., channel keypoint detection network (CKNet), to locate the user and scatterers. Subsequently, we employ a straightforward Newton optimization module to fine-tune the estimations. Experimental results demonstrate that the CKNet-empowered channel reconstruction scheme substantially reduces computational complexity while maintaining high accuracy in both user and scatterer localization and channel reconstruction.
Efficient Near-Field User Localization and Channel Reconstruction via Image Keypoint Detection
24.06.2024
2124604 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
MONOCULAR 2D SEMANTIC KEYPOINT DETECTION AND TRACKING
Europäisches Patentamt | 2023
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