An Encoder-Decoder Network for Beamforming over Sparse Large-Scale MIMO Channels

• URL: http://arxiv.org/abs/2510.02355v1
• Date: Sat, 27 Sep 2025 22:04:29 GMT
• Title: An Encoder-Decoder Network for Beamforming over Sparse Large-Scale MIMO Channels
• Authors: Yubo Zhang, Jeremy Johnston, Xiaodong Wang,
• Abstract summary: We develop an end-to-end deep learning framework for downlink beamforming in large-scale sparse MIMO channels.<n>We use three modules: (i) an encoder NN, deployed at each user end, that compresses estimated downlink channels into low-dimensional latent vectors, (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.<n>The proposed EDN beamforming framework is extended to both far-field
• Score: 10.94039589511796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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