This letter presents a proof-of-concept study of a 1 bit wide band reconfigurable intelligent surface (RIS) using planar tightly coupled dipoles (PTCD). The PTCD RIS has a planar configuration with a single dielectric layer bonded to a ground plane and is fabricated using cost-effective PCB technology. Each RIS unit element achieves binary phase shifting by altering the polarisation of the reflected wave by 180°. Co-simulation of the switch model with the RIS unit cell verifies the performance of the RIS. Passive prototypes are fabricated to demonstrate the concept, achieving a 3-dB gain bandwidth of 27.4% at the center frequency of 102 GHz, validated through simulations and measurements. Analytical calculations of the RIS response show good agreement with the simulated results.

Radio frequency fingerprinting (RFF) is the concept arising from classification of wireless emitters due to their unique radio frequency features. RFF has been further extended to applications including both RF devices classification and wireless signal identification. In this paper, we adopt Gaussian Mixture Models (GMM) technique as feature extraction approach and firstly apply it to extract RFF of antennas. 9 classical antennas with 3 different load conditions (open, short, match) were studied in our experiment. Moreover, we also made a theoretical analysis about the reason scattered signal has the unique features. Specifically, we adopt the Random Noise Radar (RNR) technique to obtain reflected RF signals of antenna under test (AUT) and apply the GMM technique to fit RF signals and then extract the RFF of AUT. A support vector machine (SVM) is proposed to recognize the RFF at different signal-to-noise ratio (SNR) environment. Compared with the conventional feature extraction approaches, for example, from variance, skewness and kurtosis (VSK) values, our method demonstrates better performance on large datasets with classification accuracy above 88% using a SVM classifier. Moreover, the accuracy remains higher than 75% even when the Signal to Noise Ratio (SNR) is equal to 0dB, indicating that the proposed approach has the strong capability of noise immunity.

The irregular subarray design technique is gaining increasing attention for its excellent compromise between cost-effective design and good beamforming performance. In this article, a novel framework that incorporates a deep reinforcement learning (DRL) technique with a gradient-descent optimization method for phase-only pattern synthesis of domino-shaped irregular arrays is proposed. By employing the deep Q-network (DQN) technique to tile domino-shaped subarrays into an array aperture, the exact phase distribution over the aperture can be derived. Furthermore, to get better performance of the scanning radiation patterns as well as improve the optimization efficiency, a novel phase-only gradient-descent optimization method is integrated with the DRL training procedure to effectively evaluate the pattern synthesis performance of the tiling configuration. Through the proposed framework by combining DQN and phase-only gradient-descent iterative algorithm, the optimal configuration for the target radiation pattern can be efficiently optimized since the subarray tiling and optimization can be realized simultaneously. The good performance of the proposed framework is demonstrated through several case studies involving 16×16, 32×32, and 64×64-dimensional domino-shaped arrays. Furthermore, full-wave simulations of both reflective metasurface arrays and antenna phased arrays, as well as measurement results from reflective metasurface arrays, are utilized to verify the calculations, thereby demonstrating the practical reliability of the developed method.

The design of a metasurface array consisting of different unit cells with the objective of minimizing its radar cross-section is a popular research topic. Currently, this is achieved by conventional optimisation algorithms such as genetic algorithm (GA) and particle swarm optimisation (PSO). One major concern of such algorithms is the extreme time complexity, which makes them computationally forbidden, particularly at large metasurface array size. Here, we apply a machine learning optimisation technique called active learning to significantly speed up the optimisation process while producing very similar results compared to GA. For a metasurface array of size 10 × 10 at a population size of ${10}^6$, active learning took 65 min to find the optimal design compared to genetic algorithm, which took 13,260 min to return an almost similar optimal result. The active learning optimisation strategy produced an optimal design for a 60 × 60 metasurface array 24× faster than the approximately similar result generated by GA technique. Thus, this study concludes that active learning drastically reduces computational time for optimisation compared to genetic algorithm, particularly for a larger metasurface array. Active learning using an accurately trained surrogate model also contributes to further lowering of the computational time of the optimisation procedure.

In this article, a generalized phased array method is proposed, which enables a 1D linear array to perform 2D beam scanning capability. Generally, the linear array is capable of achieving high efficiency beam steering along E-plane with the phase shifter. Additional phase difference to the mirrored elements is also achievable by changing the phase of reflective ground plane enables beam scanning along H-plane. Instead of using a PEC ground to mirror the dipole antenna with a fixed phase, a reflective phased-switchable structure can provide an additional phase for the dipole antenna, enabling 2D beam steering. A printed dipole linear array with a rectangular-shaped reconfigurable unit cell is introduced to demonstrate the correctness of the proposed theory. By simultaneously adjusting the phases of the antenna elements and the unit cells, wide-angle 2D scanning can be realized. And the prototype antenna is fabricated to validated the simulations and the theoretical calculations, where matches the simulations and the theory. This illustrates that the introduction of the generalized phased array theory provides an effective solution for a 1D linear array to achieve multidimensional spatial scanning capability.

To address the practical challenges of satellite-assisted mobile communication for Internet of Things (IoT) devices—such as the needs for low-profile antennas, wide-angle beam steering under strict size and power constraints, and the limited tuning flexibility of conventional reconfigurable structures—this work proposes a compact dual-polarized antenna with digitally controlled pattern steering. The method combines mirror-image theory with transmission-optics-based phase regulation, enabling beam steering through a reconfigurable digital meta-surface that replaces the conventional ground plane. A planar dual-polarized dipole is positioned above the meta-surface, and beam steering is achieved by digitally switching the p-i-n-diode states to alter the discrete reflection-phase distribution. The prototype, fabricated and characterized at 12–14 GHz, demonstrates wide-angle beam steering with stable gain, low cross-polarization, and FPGA-based real-time digital control. The proposed approach provides a compact and energy-efficient solution for next-generation satellite-assisted IoT terminals requiring flexible and robust beam steering.