This paper presents the concept, design, channel modeling, beamforming algorithm development, prototype fabrication, and experimental measurement of an electromagnetically reconfigurable fluid antenna system (ER-FAS), in which each FAS array element features electromagnetic (EM) reconfigurability. Unlike most existing FAS works that investigate spatial reconfigurability by adjusting the position and/or orientation of array elements, the proposed ER-FAS enables direct control over the EM characteristics of each element, allowing for dynamic radiation pattern reconfigurability. Specifically, a novel ER-FAS architecture leveraging software-controlled fluidics is proposed, and corresponding wireless channel models are established. Based on this ER-FAS channel model, a low-complexity greedy beamforming algorithm is developed to jointly optimize the analog phase shift and the radiation state of each array element. The accuracy of the ER-FAS channel model and the effectiveness of the beamforming algorithm are validated through (i) full-wave EM simulations and (ii) numerical spectral efficiency evaluations. These results confirm that the proposed ER-FAS significantly enhances spectral efficiency in both near-field and far-field scenarios compared to conventional antenna arrays. To further validate this design, we fabricate prototypes for both the ER-FAS element and array, using Galinstan liquid metal alloy, fluid silver paste, and software-controlled fluidic channels. The simulation results are experimentally validated through prototype measurements conducted in an anechoic chamber. Additionally, several indoor communication experiments using a pair of software-defined radios demonstrate the superior received power and bit error rate performance of the ER-FAS prototype. This paper offers a comprehensive demonstration of a liquid-based ER-FAS array for wireless communication, incorporating a novel electromagnetically reconfigurable design, channel modeling, and beamforming, supported by simulation, hardware implementation, and experimental validation.

The IMT-2030 vision emphasizes two key 6G directions: integrated sensing and communication (ISAC) alongside artificial intelligence (AI)-native frameworks, where multimodal sensory data inputs enhance situational awareness and adaptive decision-making of communication systems. Accordingly, this paper introduces a deep reinforcement learning (DRL)-based beam selection framework for downlink multi-user reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) massive MIMO (mMIMO) systems. Targeting maximized sum rates under quality of service (QoS) and fairness constraints, the framework employs two primary sensing modalities: a stereo camera mounted on the RIS for user equipment (UE) detection and inertial measurement units (IMUs) on UEs to obtain 3D Cartesian coordinates, thus eliminating the need for channel state information (CSI) acquisition. The DRL framework combines two algorithms—double deep Q-network (DDQN) and proximal policy optimization (PPO)—to jointly optimize RIS phase shifts and UE receive beamformers through predefined codebooks and adaptive beam selection. A testbed was developed to validate the system, leveraging real-world data to train the DRL algorithms. Experimental results demonstrate that both agents achieve nearoptimal sum rates across diverse base station (BS) transmit power levels and QoS thresholds while reducing computational complexity by 95%, illustrating the framework’s potential for efficient and scalable beam alignment for AI-native wireless systems.

In this work, a novel design of electromagnetically reconfigurable antennas (ERAs) based on a fluid antenna system (FAS) is proposed, and the corresponding wireless channel model is established. Different from conventional antenna arrays with static elements, the electromagnetic characteristics of each array element in the proposed ERA can be flexibly reconfigured into various states, introducing electromagnetic degrees of freedom to enhance wireless system performance. Based on the proposed ERA design, the corresponding channel model is developed. Finally, full-wave simulations are conducted to validate the overall design concept. The results reveal that a gain enhancement of 2.5 dB is achieved at a beamforming direction.

Interconnected microwave dielectric sensing nodes have the potential to revolutionize microwave material processing and design, where microwave dielectric materials characterization (MDMC) with high precision and rapid circuit design are crucial. This research presents an Internet of Things (IoT)-enabled automated MDMC system designed to tackle the non-linearity challenges in the extended cavity perturbation regime. Utilizing a cylindrical cavity operating in TE111 mode at 5 GHz, the proposed MDMC system is extensively trained on a diverse range of materials through numerous full-wave 3D electromagnetic simulations. The outputs, i.e., relative permittivity and loss tangent, are derived using advanced machine learning models, including Decision Tree (DT) and Ensemble Learning (EL). A comparative analysis that incorporates simulation, measurement, and predicted permittivity values across varying sample dimensions demonstrates the robustness and accuracy of the DT and EL model. This validates the effectiveness of our high-quality sensor node and sophisticated data processing techniques within an IoT-centric framework.

The integration of reconfigurable intelligent surfaces (RIS) and artificial noise (AN) significantly enhances physical layer security (PLS) in wireless networks, provided that RIS’s phase shifts are precisely optimized to prevent security vulnerabilities. This paper introduces a reinforcement learning (RL)based algorithm designed to optimize the phase shifts in RIS-partitioning-aided PLS systems operating in the millimeter wave (mm-Wave), without requiring channel state information (CSI) for any users. The RL algorithm optimizes the phase shifts by efficiently selecting the best beam from a predefined codebook for different partitions, which simultaneously enhances the intended signal for legitimate users and increases the effectiveness of AN on eavesdroppers, thereby maximizing the system’s secrecy capacity (SC) and addressing the inherent non-convex challenges. Additionally, the paper details the development of an experimental testbed that provides essential data to refine the algorithm. The numerical results from the testbed highlight the significant impact of RIS partitioning in PLS, which can enhance the SC by an average of 55% over the full RIS scenario, and confirm the effectiveness of the RL-based algorithm in reducing computational complexity by approximately 80% compared to the exhaustive search algorithm.

Mutual coupling is increasingly important in reconfigurable intelligent surface (RIS)-aided communications, particularly when RIS elements are densely integrated in applications such as holographic communications. This paper experimentally investigates the mutual coupling effect among RIS elements using a mutual coupling-aware communication model based on scattering matrices. Utilizing a fabricated 1-bit quasi-passive RIS prototype operating in the mmWave band, we propose a practical model training approach based on a single 3D full-wave simulation of the RIS radiation pattern, which enables the estimation of the scattering matrix among RIS unit cells. The formulated estimation problem is rigorously convex with a limited number of unknowns un-scaling with RIS size. The trained model is validated through both full-wave simulations and experimental measurements on the fabricated RIS prototype. Compared to the conventional communication model that does not account for mutual coupling in RIS, the mutual coupling-aware model incorporating trained scattering parameters demonstrates improved prediction accuracy. Benchmarked against the full-wave simulated RIS radiation pattern, the trained model can reduce prediction error by up to approximately 10.7%. Meanwhile, the S-parameter between the Tx and Rx antennas is measured, validating that the trained model exhibits closer alignment with the experimental measurements. These results affirm the accuracy of the adopted model and the effectiveness of the proposed model training method.

Silicon-based complementary metal oxide semiconductor (CMOS) process has become one of the most popular processes to realize system-on-chip (SoC). However, as one of the essential components of wireless SoC, antennas are typically suffering from the poor radiation because of the highly conductive silicon substrate. Such antennas are known as antenna-on-chip (AoC). To enhance the radiation performance of AoC, artificial magnetic conductors (AMC) with double periodic strip structure layers has been proposed in this paper that can not only provide in-phase reflection but also isolate the antenna from the lossy silicon substrate. The proposed AMC shows a gain enhancement of 4.5 dB. The AMC-backed AoC is well-matched within 77-125 GHz and provides a boresight gain of 2 dBi at 94 GHz.