ABSTRACT In modern communication networks, optimizing radiation patterns is essential for ensuring high‐quality signal transmission, directional beamforming, and efficient spectrum utilization. Traditional methods often struggle with nonlinearity, high dimensionality, and limited adaptability to dynamic and complex beam requirements. Therefore, this research proposes a novel model of enhancing antenna array performance through hybrid deep learning for accurate beam coefficient prediction in complex communication networks (AAP‐HybDL‐BCP). Here, complex communication networks refer to a dynamic 5G/6G environment with rapid beam switching, interference, and varying user conditions—not a complex antenna structure. While a simple 4 × 4 isotropic array is adopted for evaluation, the proposed model is designed to effectively deal with the challenges in such real‐world network scenarios. These radiation pattern images are provided as input to a hybrid deep learning framework composed of finite element interpolated neural network (FEINN) for phase prediction and the multi‐anchor space‐aware temporal convolutional neural network (MSATCNN) for amplitude prediction. This dual‐network approach significantly enhances the reliability and precision of beam coefficient prediction for next‐generation reconfigurable antenna systems. Then, the proposed AAP‐HybDL‐BCP is implemented and the performance metrics like root‐mean‐square error (RMSE), R ‐square, peak signal‐to‐noise ratio (PSNR), structural similarity index measure (SSIM), and peak beam great circle distance (GCD) are examined. Finally, the proposed AAP‐HybDL‐BCP method achieves a lower MSE of 0.0312 and a higher PSNR of 19.57 compared with existing approaches such as CNN‐ANFP‐SM, SB‐DNN‐CBFA, and LC‐ML‐MWBP, demonstrating its superior prediction accuracy.
Arunarasi et al. (Sun,) studied this question.
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