Traditional multiple-input multiple-output (MIMO) systems are confronted with significant challenges in realizing ubiquitous connectivity for sixth-generation (6G) networks, particularly in environments characterized by severe signal blockage and dynamic co-mobility. While aerial intelligent reflecting surfaces (AIRS) offer a promising paradigm to address these difficulties, the existing channel models often fail to capture fast channel changes, thereby leading to inefficient phase optimization in time-varying scenarios. To address these limitations, a geometric MIMO channel model is proposed for AIRS-assisted communications. This model comprises an indirect link from the base station (BS) via the AIRS to the receiver (Rx) and a direct BS-Rx link, whose direct propagation environment is rigorously characterized by a one-cylinder model specifically designed to tackle the complexities of dynamic co-mobility and intricate propagation. A joint optimization problem is formulated to maximize the achievable rate by optimizing the transmitted signal’s covariance matrix and the AIRS phase shift. Subsequently, an iterative algorithm employing the projected gradient method (PGM) is proposed for its solution, which is tailored for efficient operation in time-varying environments. Furthermore, expressions for the space–time correlation function and Doppler power spectrum are derived to evaluate the overall channel properties. Significant enhancements in achievable rates are demonstrated by AIRS, with substantial gains being observed even for a small number of reflecting elements. Consequently, crucial guidance for the design of robust AIRS-assisted MIMO systems is provided by these findings, and the broad applicability of the proposed algorithm is thereby confirmed.

Millimeter-wave (mmWave) and terahertz (THz) communications are susceptible to frequent link disruptions and severe performance degradation due to high directionality, significant path loss, and sensitivity to blockages. These challenges are particularly acute in highly dynamic and densely populated user environments. The issues present significant obstacles to ensuring reliability and quality of service (QoS) in future space–air–ground integrated networks. To address these challenges, this paper proposes an adaptive transmission control scheme designed for space–air–ground integrated multi-hop networks operating in the mmWave/THz bands. By analyzing the intermittent connectivity inherent in such networks, the proposed scheme incorporates an incremental factor and a backlog indicator into its congestion control mechanism. This allows for the accurate differentiation between packet losses resulting from network congestion and those caused by channel blockages, such as human body occlusion or beam misalignment. Furthermore, the scheme optimizes the initial congestion window during the slow-start phase and dynamically adapts its transmission strategy during the congestion avoidance phase according to the identified cause of packet loss. Simulation results demonstrate that the proposed method effectively mitigates throughput degradation from link blockages, improves data transmission rates in highly dynamic environments, and sustains more stable end-to-end connectivity. Our proposed scheme achieves a 35% higher throughput than TCP Hybla, 40% lower latency than TCP Veno, and maintains 99.2% link utilization under high mobility.

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.

Reconfigurable intelligent surface (RIS) emerges as an efficient and promising technology for the next wireless generation networks and has attracted a lot of attention owing to the capability of extending wireless coverage by reflecting signals toward targeted receivers. In this paper, we consider a RIS-assisted high-speed train (HST) communication system to enhance wireless coverage and improve coverage probability. First, coverage performance of the downlink single-input-single-output system is investigated, and the closed-form expression of coverage probability is derived. Moreover, travel distance maximization problem is formulated to facilitate RIS discrete phase design and RIS placement optimization, which is subject to coverage probability constraint. Simulation results validate that better coverage performance and higher travel distance can be achieved with deployment of RIS. The impacts of some key system parameters including transmission power, signal-to-noise ratio threshold, number of RIS elements, number of RIS quantization bits, horizontal distance between base station and RIS, and speed of HST on system performance are investigated. In addition, it is found that RIS can well improve coverage probability with limited power consumption for HST communications.