@inproceedings{8a9b6b42b3da4cdb8f1d9d81bd4c3d81,
title = "Hardware Acceleration of Pattern Synthesis CNN for Array Antennas Based on FPGA",
abstract = "This paper presents an FPGA-based accelerated neural network implementation for large-scale array antenna radiation pattern synthesis, addressing the critical challenges of real-time adaptive beam scanning. The methodology combines a convolutional neural network designed for radiation pattern synthesis with efficient FPGA deployment using the Vitis AI tools. Experimental validation on a 1024-element array antenna pattern synthesis demonstrates superb performance: sub-0.1° beam pointing accuracy, sidelobe level deviations constrained to within 2 dB, while reducing the synthesis time from 163.6 ms (GPU implementation) to 4.7 ms (35-fold speedup).",
keywords = "CNN, FPGA, synthesis of antenna arrays",
author = "Zhikuo Li and Xiang Li and Ming Bai",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization for RF, Microwave, and Terahertz Applications, NEMO 2025 ; Conference date: 29-07-2025 Through 01-08-2025",
year = "2025",
doi = "10.1109/NEMO62710.2025.11215257",
language = "英语",
series = "IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization for RF, Microwave, and Terahertz Applications, NEMO 2025 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization for RF, Microwave, and Terahertz Applications, NEMO 2025 - Proceedings",
address = "美国",
}