Electromagnetic illusion technology offers a disruptive approach to advanced camouflage by precisely tailoring the object's scattering signature. Over recent decades, the emergence of metamaterials and metasurfaces has revolutionized this field. However, conventional electromagnetic illusion devices typically employ reflective arrays to reconstruct electromagnetic field distributions for basic demonstrations, resulting in limitations such as feed blockage, optical occlusion, and prohibitive computational costs, which are further exacerbated in broadband applications. Here, an optically transparent transmissive metasurface (TTM), strategically designed using conditional generative adversarial networks, is proposed, which can not only generate user‐defined broadband electromagnetic illusions closely resembling specified targets but also significantly shorten the inverse design cycle compared to traditional iterative optimization. Full‐wave simulations and microwave measurements confirm the proposed TTM's broadband robustness and immunity to feed blockage and visual obstruction. The prototype exhibits a light transmittance of 71.5% and produces high‐fidelity illusions across 12 to 18 GHz, with forward‐scattering radar‐cross‐section patterns maintaining >90% correlation with the desired profiles. By integrating transparent photonic engineering with deep‐learning‐assisted inverse design, this work establishes a versatile platform for intelligent electromagnetic manipulation and paves the way for adaptive camouflage systems that reconcile optical transparency with advanced radio‐frequency deception.

Most electromagnetic invisibility devices are designed while relying on rigid structures, which have limitations in adapting to complex curved surfaces and dynamic deployment. In contrast, flexible invisibility structures have great application value due to their bendable and easy-to-fit characteristics. In this paper, we propose a flexible metasurface suitable for broadband SAR (Synthetic Aperture Radar) imaging invisibility, which realizes multi-domain joint regulation of electromagnetic waves by designing two subwavelength unit structures with differentiated reflection characteristics and combining array inverse optimization methods. The metasurface employs a sponge-like dielectric substrate and integrates resistive ink to construct a resonant structure, which can suppress electromagnetic scattering through joint phase and amplitude modulation, achieving low detectability of targets in UAV (Unmanned Aerial Vehicle) detection scenarios. Indoor microwave anechoic chamber tests and outdoor UAV-borne SAR experiments verify its stable invisibility performance in a wide frequency band, providing theoretical and experimental support for the application of flexible metasurfaces in dynamic electromagnetic detection countermeasures.

In multi-user wireless communication scenarios, signal degradation caused by channel fading and co-channel interference restricts system capacity, while traditional enhancement schemes face challenges of high coordination complexity and hardware integration. This paper proposes an electromagnetic focusing method using a single-layer transmissive passive metasurface. A high-efficiency metasurface array is fabricated based on PCB technology, which utilizes subwavelength units for wide-range phase modulation to construct a multi-user energy convergence model in the WiFi band. By optimizing phase gradients through the geometric phase principle, the metasurface achieves collaborative wavefront manipulation for multiple target regions with high transmission efficiency, reducing system complexity compared to traditional multi-layer structures. Measurements in a microwave anechoic chamber and tests in an office environment demonstrate that the metasurface can simultaneously create signal enhancement zones for multiple users, featuring stable focusing capability and environmental adaptability. This lightweight design facilitates deployment in dense networks, providing an effective solution for signal optimization in indoor distributed systems and IoT communications.

Metamaterials, leveraging the unique characteristics of subwavelength structures, have led to significant research progress in fields such as acoustics, optics, and electromagnetics

The emergence of 5G represents a pivotal step in merging mobile communication networks with the Industrial Internet of Things. Despite the numerous advantages of 5G, the presence of unknown obstacles can adversely affect user signals. Although mitigating signal pressures can be achieved by increasing base station density, it often involves bulky equipment and high costs. To address this, we propose a deep learning-based method for controlling tunable transmissive metasurfaces and validate their scattering control capabilities in the presence of obstacles. By constructing a network model to analyze the mapping relationship between metasurface arrays and far-field scattering, rapid control of scattering characteristics is achieved. AI-driven high-performance tunable metasurfaces exhibit vast potential applications in intelligent communication, offering a universal solution for intelligent control in complex signal environments.