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. Firstly, 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 CNN-LSTM-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. 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.

Deep learning plays a vital role in the design of electromagnetic (EM) structures. However, in current research, a single neural network typically supports only one structure design and requires a complex framework to accommodate multiple structure designs. This paper proposes using one neural-assist design for facilitating multiple EM structures. We employ two filling methods to control the vector length, an identification method to ensure accurate prediction results, and a random auxiliary vectors method to increase the data volume and reduce loss. Subsequently, we design a forward neural network (FNN) and an inverse neural network (INN) using the proposed method. The developed neural network is used to complete the dual-passband frequency selective surface (DP-FSS), space-time-coding digital metasurface element (STCDME), single/dual absorbing metasurface (SDAM), and dual-stopband frequency selective surface (DS-FSS) designs. The mean absolute error (MAE) loss values for the FNN/INN predictions and actual results for these four structures are 0.019/0.116, 0.035/0.602, 5.14/0.146, and 0.018/0.07, respectively. Finally, we design four structures with the well-training network, fabricate DP-FSS and DS-FSS, and measure them in an anechoic chamber. The measurement and simulation results are in good agreement. The proposed method significantly reduces the complexity of multiple EM structure designs, decreases the need for multiple neural networks, and simplifies the design framework, thereby contributing to the development of AI-assisted EM structures.

In recent years, jamming strategies for Synthetic Aperture Radar (SAR) pertaining to target detection and identification, such as the creation of false targets, electromagnetic (EM) deception, and signal spoofing, have been increasingly emphasized. Distinct from traditional SAR jamming approaches, the introduction of an innovative artificial material cloak in SAR target jamming presents augmented capabilities. These methods demonstrate a proficient redirection of incident EM waves in specific or arbitrary directions, effectively masking the vital information linked to critical targets. This study introduces a broadband SAR target jamming system employing an information metasurface that incorporates intelligent information processing algorithms in conjunction with a space-time-coding digital metasurface, endowing it with the capacity to adeptly modulate incident EM waves. This integration facilitates a versatile approach to jamming, enabling the deployment of multi-mode protective measures against critical targets. The conducted simulation and experiment results validate the system’s ability to adjustably produce EM deception and generate multiple false targets independently of the SAR system. The outcomes of this research significantly advance the practicality of SAR protection strategies, pushing the boundaries toward more dynamic, broadband, and controllable scenarios, thereby substantially improving the concealment of critical targets in highly sensitive conflict areas.

This article presents a fully automated inverse–forward design of multiple electromagnetic (EM) structure designs by using a requirement-based multiple inverse–forward neural network (M-IFNN). Compared to the traditional design method, the proposed method can obtain the structure model, geometric parameters, and $S$ -parameters by just inputting sentences into a vector-extracted language model, which output to M-IFNN. In addition, we use the parallel-combined method to achieve inverse–forward output by only one neural network for the first time. To achieve the above target, first, we use three feature extraction algorithms to obtain the feature vector of three metasurfaces. Then, we use identification vector and filling methods to achieve multi-EM designs by one neural network. Subsequently, we use the parallel-combined method to integrate the inverse geometric parameters design and forward-parameters design into one model. Deep neural network (DNN) is chosen for M-IFNN because it has smaller storage memory and good results. The M-IFNN finally achieves a 100% correctness prediction of corresponding structures and a 0.41/0.034 mean absolute error (MAE) result for predicting inverse/forward geometric parameters/ $S$ -parameters. Three prototypes are fabricated and measured. The measurement results are in good agreement with the simulation results. The good validation results prove the feasibility of the proposed method. Additionally, several dialectical discussions about M-IFNN are at the end of the article.

Synthetic aperture radar (SAR) is a high-resolution imaging radar. With the deteriorating electromagnetic environment, SAR systems are susceptible to various forms of electromagnetic interference, including deception jamming. This jamming notably impacts SAR signal processing and subsequently worsens the quality of acquired images. Chaotic frequency modulation (CFM) signals could effectively counteract deception jamming. Nevertheless, due to the inadequate band-limited performance of CFM signals, higher oversampling factors are needed for achieving optimal low sidelobe levels, leading to increased system costs and excessively high data rates. Additionally, not all chaotic sentences meet the CFM signal requirements. In response, this paper proposes a novel signal modulation method called chaotic-nonlinear frequency modulation (C-NLFM) that enhances band-limited performance. The optimum parameters for C-NLFM signals are selected using the particle swarm optimization (PSO) algorithm. In this way, C-NLFM signals attain ideal low sidelobe levels even when faced with reduced oversampling factors. Meanwhile, this chaotic coding mode retains its capability to effectively suppress deception jamming. Moreover, C-NLFM signals demonstrate versatility in adapting to various chaotic sequences, thereby enhancing their flexibility to modify the signals as required. Through comprehensive simulations, data analysis, and a semi-physical experiment, the effectiveness and superiority of this proposed method have been confirmed.

In this paper, a design method of a broadband metasurface antenna with low cross-polarization based on characteristic theory is presented. Firstly, the mode field distribution of the metasurface is analyzed, and multiple specific characteristic modes are effectively excited through the magnetic current gap. Then the appropriate characteristic mode is selected to form a broadband radiation metasurface antenna. This design solves the inherent problems of high Q, high insertion loss and narrow frequency band of traditional microstrip antennas.