{"ID":2864875,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.02355","arxiv_id":"2510.02355","title":"An Encoder-Decoder Network for Beamforming over Sparse Large-Scale MIMO Channels","abstract":"We develop an end-to-end deep learning framework for downlink beamforming in large-scale sparse MIMO channels. The core is a deep EDN architecture with three modules: (i) an encoder NN, deployed at each user end, that compresses estimated downlink channels into low-dimensional latent vectors. The latent vector from each user is compressed and then fed back to the BS. (ii) a beamformer decoder NN at the BS that maps recovered latent vectors to beamformers, and (iii) a channel decoder NN at the BS that reconstructs downlink channels from recovered latent vectors to further refine the beamformers. The training of EDN leverages two key strategies: (a) semi-amortized learning, where the beamformer decoder NN contains an analytical gradient ascent during both training and inference stages, and (b) knowledge distillation, where the loss function consists of a supervised term and an unsupervised term, and starting from supervised training with MMSE beamformers, over the epochs, the model training gradually shifts toward unsupervised using the sum-rate objective. The proposed EDN beamforming framework is extended to both far-field and near-field hybrid beamforming scenarios. Extensive simulations validate its effectiveness under diverse network and channel conditions.","short_abstract":"We develop an end-to-end deep learning framework for downlink beamforming in large-scale sparse MIMO channels. The core is a deep EDN architecture with three modules: (i) an encoder NN, deployed at each user end, that compresses estimated downlink channels into low-dimensional latent vectors. The latent vector from eac...","url_abs":"https://arxiv.org/abs/2510.02355","url_pdf":"https://arxiv.org/pdf/2510.02355v1","authors":"[\"Yubo Zhang\",\"Jeremy Johnston\",\"Xiaodong Wang\"]","published":"2025-09-27T22:04:29Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.IT\",\"cs.LG\",\"eess.SP\"]","methods":"[]","has_code":false}