Port and radiation pattern decoupled metasurface-loaded patch antenna using deep-learning-assisted optimization for MIMO applications

A metasurface-loaded 1×2 patch array antenna assisted by a deep-learning optimization method is proposed to realize port and radiation pattern decoupling simultaneously to enhance the isolation among elements in multi-input multi-output (MIMO) systems. The deep-learning-assisted optimization method uses an artificial neural network (ANN) and a particle swarm optimization (PSO) algorithm to seek the optimal structure of the antenna to achieve port decoupling with undistorted radiation patterns. The ANN is trained to describe the nonlinear relationship between the geometric parameters and the responses of the antenna. The PSO algorithm, guided by the cost function and number of iterations, is used to optimize the structure of the antenna according to the cost function combined with the trained ANN. Finally, by constraining the cost function, we obtain a 1×2 patch array antenna with a metasurface fixed above by studs, which achieves port and radiation pattern decoupling simultaneously. To validate the principle and design method, we designed, fabricated, and measured an antenna prototype with dimensions of 0.88λ0×0.47λ0×0.21λ0 (λ0 is the wavelength in free space at the center frequency). The measured fractional bandwidth is 8% (4.8–5.2 GHz). The isolation of the two-element patch antenna increases from 7.6 dB to 24.3 dB with an envelope correlation coefficient (ECC) of <0.0005 at 0.35λ0. Moreover, the H-plane radiation pattern of each element is consistent and symmetric in the broadside direction. These characteristics make the proposed antenna suitable for MIMO antenna systems with close spacing.