QueryFieldNet: A Query-Conditioned Surrogate Model for Multi-Beam Reflectarray Principal-Cut Gain Prediction

This study proposes QueryFieldNet for predicting two orthogonal principal-cut wide-angle gain curves of fixed-θ-grid multi-beam reflectarrays. The balanced seven-point θ-grid dataset {−45°,−30°,−15°,0°,15°,30°,45°} contains 19 × 19 phase maps, 1- to 5-bit phase quantization, and one to four intended beams. QueryFieldNet combines periodic phase inputs, multi-scale 2D residual encoding, cross-attention decoding with 181 Fourier-θ queries, intended-beam metadata supervision, and beam-basis residual refinement. Three proposed metrics, Multi-Beam HPBW RMSE, Multi-Beam Main-Lobe RMSE, and Beam-Peak RMSE, quantify the local −3 dB, −10 dB, and peak-gain errors around each intended beam. Across three runs, the final QueryFieldNet obtains bit-wise macro-averaged RMSE 3.240±0.143, MAE 1.975±0.134, R20.99792±0.00017, Multi-Beam HPBW RMSE 1.872±0.015, Multi-Beam Main-Lobe RMSE 2.134±0.047, and Beam-Peak RMSE 1.730±0.065. The aggregate local-window values combine both principal cuts; for ϕ=90°, they represent target-centered local errors rather than physical intended-beam HPBW or peak measurements. These results concern an array-factor-level synthetic benchmark; full-wave and measurement-based validation remain future work.