Fig. 4: Schematic diagram LIVG Building and Optimization: PSO case in scan angle 0° to 30°, each step 30°/128.
From: Dimensionality reduced antenna array for beamforming/steering
Deep Learning Case input data set consist of scan angle = m(m = 15,20,…,85); element = n(n = 4,5,6,…,16), rank = n-1;output data set was the optimized LIVG. a M = 128, N = 16 Grey-scale map represents phase shifts [−180°,180°]at element n, degree m; b Normalized Ideal Far-field pattern(red) form 0° to 30°; c Grey-scale map after SVD compressed. d Grey-scale map reconstructed by LIVG. e Reconstructed weight matrix far-field pattern(blue) from 0° to 30°. f Iterative convergence of PSO algorithm. g The optimally constructed LIVG, where each row vector of the LIVG represents a specific beam pointing: 0°(blue),8°(purple), 12°(yellow), 30°(red). h Normalized Ideal far-field pattern(red with shadow) and optimal LIVG-built normalized far-field pattern(blue with shadow). i Deep Learning Data Set building; j Topology of Transformer Deep Learning Network Structure; k Far-field pattern of the LIVG matrix generated by the deep learning model (dashed line) and PSO algorithm (solid line).
