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Physics-Informed Deep Learning Method for Real-Time Multi-Harmonic Beamforming Based on Space-Time-Coding Metasurface
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DOI：10.1002/aelm.202500595.png)
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Abstract
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Space-time-coding metasurfaces (STCMs) enable simultaneous controls of electromagnetic wave across multiple harmonics, but designing high-performance coding sequences in real time remains challenging. Here, we propose an unsupervised physics-informed deep learning framework that can generate optimal spatiotemporal coding patterns for arbitrary single- and dual-beam requirements at each harmonic frequency. The proposed method features three key innovations: physics-informed mechanisms to enable unsupervised learning without requiring paired training data, a dedicated strategy for multi-bit metasurface configurations, and the Conflict Averse Gradient descent (CAGrad) method to coordinate the parameter optimization across harmonics in multi-task learning. Experiments on a 2-bit STCM demonstrate robust beamforming capabilities over five harmonics, achieving an average radiation difference of 1.55 dB and real-time design <0.1s. This is a 4-order-of-magnitude improvement in computational efficiency compared with the particle swarm optimization methods. This work establishes a real-time and physics-aware design paradigm for intelligent metasurfaces in the next-generation wireless systems.
Keywords:
multi-harmonic beamforming
physics-informed deep learning
space-time-coding metasurface
unsupervised learning
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