Effective Artificial Neural Network Framework for Time-Modulated Arrays Synthesis

• Published in: IEEE transactions on antennas and propagation 2023-10, Vol.71 (10), p.1-1
• Main Authors: Hei, Yong Qiang, Ma, Long Yuan, Li, Wen Tao, Mou, Jin Chao, Shi, Xiao Wei
• Format: Article
• Language: English
• Subjects: Amplitude modulation; Antenna arrays; array synthesis; Artificial neural network; Artificial neural networks; Coders; Decoding; Equivalence; Excitation; focused beampattern; Harmonic analysis; Neural networks; Optimization; Optimization techniques; Phased arrays; shaped beampattern; SISO (control systems); Switches; Synthesis; time-modulated antenna array
• ISSN: 0018-926X; 1558-2221
• DOI: 10.1109/TAP.2023.3303464

The antenna array synthesis problem has long been known as a tough issue, which attracts considerable interests to explore high-performance low-complexity optimization techniques. In this paper, an efficient artificial neural network (ANN) for time-modulated arrays (TMA) synthesis is proposed. By defining the equivalent excitation properly, TMA synthesis can be first transformed to a generalized phased array optimization. Next, a two-stage ANN framework composed of two encoders and a universal decoder is established to optimize the equivalent excitation, and then a single-input single-output (SISO) sinc -1 -ANN is proposed to solve inverse of sinc(·) efficiently. To achieve fast and accurate pattern prediction, the decoder is pre-trained to be a real-time array analyzer, while the encoder is designed as an array synthesizer to develop online training. By minimizing the loss function related to radiation pattern and equivalent excitation, the desired pattern can be achieved. Then, with the help of the pre-trained SISO sinc -1 -ANN, the static excitation coefficient, switch-on duration and starting time are acquired. Simulation results of different types of desired TMA patterns are provided to verify the superiority, effectiveness and efficiency of the proposed approach.