Space-Time-Coding Digital Metasurface Element Design Based on State Recognition and Mapping Methods With CNN-LSTM-DNN

Space-time-coding digital metasurface has drawn worldwide attention with the ability to improve communication quality and change the direction of electromagnetic (EM) wave propagation in real time. This article proposes a deep-learning-assisted method to design the space-time-coding digital metasurface element (STCDME) with the state recognition and mapping technique methods. Compared to traditional pure EM simulation methods to simulate all states, the proposed method fully considers the relationship between different states of STCDME to accelerate the design. First, we use the state recognition method to distinguish the S-parameters’ states. Subsequently, with the state mapping method, we use one state phase to predict the other states’ S-parameters. Simulation time is reduced by half with the proposed methods. Various algorithms are compared, and finally, the convolutional neural network (CNN)-long short-term memory (LSTM)-deep neural network (DNN) (CLD) hybrid algorithm is chosen for the proposed methods, which is also the first instance of using the CLD algorithm to design the EM structure. The proposed method is validated with two design examples. An element prototype is made and measured with the vector network analyzer (VNA). The measurement results agree with the simulation results. We then use the designed STCDME to complete the design of the beamforming array, which realizes the beamforming at 0°, 15°, 30°, and 45° angles. Finally, we briefly discuss the application/drawbacks of the proposed methods and the CLD model in EM fields.