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A Planar Array Synthesis Method Based on Deep Learning and Radiation Pattern Superposition Method
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DOI：10.1109/TAP.2025.3564705.png)
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Abstract
En 中文
A dual-branched convolutional neural network (CNN) integrated with hybrid training criteria is proposed for real-time multibeam synthesis in planar uniform antenna arrays. For higher speed in dataset generation, the pattern superposition method is introduced in this article, which significantly improves data generation speed (~0.7 ms per each). With a new hybrid training strategy integrating both “data-driven approximation” and “physics-informed approximation” criteria, flexible training while maintaining model synthesis efficiency is achieved simultaneously. Based on the approaches mentioned above, the proposed dual-branched CNN enables a dynamic multiobjective balance between its two branch outputs, accomplishing amplitude-phase synthesis for antenna array design. In the numerical simulation, consistent sidelobe suppression ( $20\sim 40$ dB) is demonstrated for dual-beam steering across $10^{\circ }{\sim }20^{\circ }$ off-axis angles, with high synthesis speeds (~0.05 s per each) on suitable computing platforms. In comparison experiments, the proposed method outperforms typical population-based stochastic optimization algorithms and deep neuron network (DNN) structure in both synthesis efficiency and pattern regularity. The model’s real-time capability is verified for beam reconfiguration scenarios requiring subsecond responses, suggesting a new pathway for physics-embedded deep learning in antenna array synthesis.
Keywords:
Beamforming
convolutional networks
deep learning
planar arrays
Journal
IF:
5.8
Papers:
502
Citations:
6.8W