Transformer-based Scalable Beamforming Optimization via Deep Residual Learning

• URL: http://arxiv.org/abs/2510.13077v1
• Date: Wed, 15 Oct 2025 01:43:51 GMT
• Title: Transformer-based Scalable Beamforming Optimization via Deep Residual Learning
• Authors: Yubo Zhang, Xiao-Yang Liu, Xiaodong Wang,
• Abstract summary: unsupervised deep learning framework for downlink beamforming in large-scale MU-MISO channels.<n>Model is trained offline, allowing real-time inference through lightweight feedforward computations in dynamic communication environments.
• Score: 12.79709425087431
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We develop an unsupervised deep learning framework for downlink beamforming in large-scale MU-MISO channels. The model is trained offline, allowing real-time inference through lightweight feedforward computations in dynamic communication environments. Following the learning-to-optimize (L2O) paradigm, a multi-layer Transformer iteratively refines both channel and beamformer features via residual connections. To enhance training, three strategies are introduced: (i) curriculum learning (CL) to improve early-stage convergence and avoid local optima, (ii) semi-amortized learning to refine each Transformer block with a few gradient ascent steps, and (iii) sliding-window training to stabilize optimization by training only a subset of Transformer blocks at a time. Extensive simulations show that the proposed scheme outperforms existing baselines at low-to-medium SNRs and closely approaches WMMSE performance at high SNRs, while achieving substantially faster inference than iterative and online learning approaches.

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