Zhuxian Lian

Motivated by the transformative potential of integrating uncrewed aerial vehicles (UAVs) and stacked intelligent metasurfaces (SIMs) into low-altitude aerial intelligent networks (LAINs), this article provides a comprehensive investigation of UAV-SIM technologies within integrated sensing and communication (ISAC) systems. By synergizing physical mobility with real-time electromagnetic wavefront manipulation, the UAV-SIM paradigm is poised to significantly enhance system performance across diverse operational scenarios. Despite these promising prospects, deploying UAV-SIM technology in LAINs faces substantial bottlenecks, particularly in the accurate characterization of complex air-to-ground propagation environments and the design of efficient optimization algorithms. To address these challenges, we propose a unified channel model-driven optimization framework tailored to ensure reliable quality of service for UAV-SIM-enabled LAINs. Utilizing the 3GPP TR 38.901 channel model as a realistic benchmark, this framework establishes a rigorous foundation for synthesizing and validating resource allocation strategies, ultimately yielding superior system capacity and reliability. Furthermore, to improve the robustness and efficiency of end-to-end information processing, we develop a deep reinforcement learning (DRL) scheme based on the mixture-of-experts architecture. This specialized approach facilitates the optimal management of network resources, thereby enabling highly efficient signal transmission. Finally, we outline open research directions and emerging trends to stimulate future investigations in the realm of UAV-SIM-enabled LAINs.

In this work, we investigate covert communication with a cognitive jammer (CJ) embedded in a public communication system. Specifically, while the transmitter continuously transmits public messages to the receiver, it may also transmit its covert messages opportunistically, and a CJ is utilized to assist the covert communication. To evaluate the covert performance, we derive the detection error probability (DEP) for both the CJ and the warden, as well as the average covert rate (ACR). Our analysis indicates that applying a CJ to assist covert communication embedded in a public communication system can outperform the scheme with an uninformed jammer or without a jammer in terms of covertness. A high public transmission rate can degrade the covertness; however, improving the detection accuracy of the CJ can contribute to enhanced performance in terms of both covertness and ACR.

This article addresses the impact of asymmetric jitter, which arises from air turbulence or mechanical vibrations, on the 3-D attitude angles of an aerial intelligent reflecting surface (AIRS) mounted on an uncrewed aerial vehicle (UAV). To characterize these effects, a physics-based channel model based on the spherical wavefront assumption (SWA) is established. To mitigate the resulting performance degradation, we propose a novel continuous reflection phase based on the planar wavefront assumption (PWA), leveraging the concept of the Zadoff-Chu sequence. This design integrates a conventional reflection phase component with a spatial–frequency–bandwidth-dependent term, effectively broadening the bandwidth of the passive beam. Using the proposed continuous reflection phase, we analyze the normalized array gain function and average received signal power under UAV jitter, deriving approximate expressions for these metrics using Fresnel functions. The analysis demonstrates that the proposed reflection phase can expand the beam bandwidth to cover the potential range of jitter angles. Furthermore, a discrete reflection phase is designed based on the continuous version. Numerical results confirm that the beam bandwidth remains stable as the severity of asymmetric jitter increases, illustrating the effectiveness of the designed phases in mitigating UAV platform instability. Additionally, the results indicate that the proposed reflection phase can effectively compensate for performance degradation caused by UAV jitter.

This paper develops a physically consistent precoding framework for extremely large antenna arrays (ELAAs), incorporating structural mutual coupling through a two-dimensional impedance network. To maintain scalability, we introduce a Neumann series approximation for the inverse coupling operator. Our analysis reveals that coupling-aware received power maximization reduces to a Hermitian rank-one quadratic form, whose optimum aligns with the dominant eigendirection of the effective coupling-shaped channel. This result indicates that both eigen-decomposition-based optimization and coupling-aware maximum ratio transmission (MRT) enhance power efficiency under mutual coupling, with the eigenmode design achieving superior performance. In addition, we further extend the analysis from the free-space path to the multipath scenario, demonstrating the robustness and adaptability of the proposed method under practical propagation conditions. Simulations confirm that structural coupling severely degrades conventional MRT, whereas the proposed eigenmode method with Neumann approximated coupling attains the highest received power among all considered schemes. The framework is interpretable, numerically stable, and readily implementable, offering practical guidance for energy-efficient near-field beamforming on ultra-large apertures.

Covert communication assisted by unmanned aerial vehicles (UAVs) can achieve a low detection probability in complex environments through auxiliary strategies, including dynamic trajectory planning and power management, etc. This paper proposes a dual-UAV scheme, where one UAV transmits covert information while the other one generates stochastic jamming to disrupt the eavesdropper and reduce the probability of detection. We propose a dual-mode jamming scheme which can efficiently enhance the average covert rate (ACR). A joint optimization of the dual UAVs’ flight speeds, accelerations, transmit power, and trajectories is conducted to achieve the maximum ACR. Given the high complexity and non-convexity, we develop a dedicated algorithm to solve it. To be specific, the optimization is decomposed into three sub-problems, and we transform them into tractable convex forms using successive convex approximation (SCA). Numerical results verify the efficacy of dual-mode jamming in boosting ACR and confirm the effectiveness of this algorithm in enhancing CC performance.

To effectively capture the inherent near-field effects and spatial non-stationarity across extremely large antenna arrays (ELAAs), this letter develops a novel analytical channel model tailored for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. In the proposed framework, spatial non-stationarity is first characterized using a 0-1 diagonal matrix, after which the composite XL-MIMO channel matrix is formulated as a linear combination of structured matrix components. Leveraging this representation, we perform a comprehensive performance analysis, evaluating key metrics including the downlink ergodic capacity, an efficiently computable upper bound derived from eigenvalue matrix, and the symbol error probability (SEP). The analysis demonstrates that the proposed scheme not only achieves performance comparable to benchmark methods but also substantially reduces computational complexity. Furthermore, the analysis reveals a pronounced performance degradation in the presence of increasing channel estimation errors.