In this paper, we study the trajectory and resource allocation design for downlink energy-efficient secure unmanned aerial vehicle (UAV) communication systems, where an information UAV assisted by a multi-antenna jammer UAV serves multiple ground users in the existence of multiple ground eavesdroppers. The resource allocation strategy and the trajectory of the information UAV, and the jamming policy of the jammer UAV are jointly optimized for maximizing the system energy efficiency. The joint design is formulated as a non-convex optimization problem taking into account the quality of service (QoS) requirement, the security constraint, and the imperfect channel state information (CSI) of the eavesdroppers. The formulated problem is generally intractable. As a compromise approach, the problem is divided into two subproblems which facilitates the design of a low-complexity suboptimal algorithm based on alternating optimization approach. Simulation results illustrate that the proposed algorithm converges within a small number of iterations and demonstrate some interesting insights: (1) the introduction of a jammer UAV facilitates a highly flexible trajectory design of the information UAV which is critical to improving the system energy efficiency; (2) by exploiting the spatial degrees of freedom brought by the multi-antenna jammer UAV, our proposed design can focus the artificial noise on eavesdroppers offering a strong security mean to the system.

In this paper, we study the resource allocation and trajectory design for secure unmanned aerial vehicle (UAV)-enabled communication systems, where multiple multi-purpose UAV base stations are dispatched to provide secure communications to multiple legitimate ground users (GUs) in the existence of multiple eavesdroppers (Eves). Specifically, by leveraging orthogonal frequency division multiple access (OFDMA), active UAV base stations can communicate to their desired ground users via the assigned subcarriers while idle UAV base stations can serve as jammer simultaneously for communication security provisioning. To achieve fairness in secure communication, we maximize the average minimum secrecy rate per user by jointly optimizing the communication/jamming subcarrier allocation policy and the trajectory of UAVs, while taking into account the constraints on the minimum safety distance among multiple UAVs, the maximum cruising speed, the initial/final locations, and the existence of cylindrical no-fly zones (NFZs). The design is formulated as a mixed integer non-convex optimization problem which is generally intractable. Subsequently, a computationally-efficient iterative algorithm is proposed to obtain a suboptimal solution. Simulation results illustrate that the performance of the proposed iterative algorithm can significantly improve the average minimum secrecy rate compared to various baseline schemes.

This paper presents a quad-polarization reconfigurable multi-faceted conformal array of wide coverage range and high gain that is suitable for unmanned aerial vehicle (UAV) communication applications. The design features a sparse array layout comprised of three conformal subarrays, where each subarray includes nine quad-polarization reconfigurable radiating elements. The radiating element is a single-port excited patch radiator loaded by edge slots for size miniaturization and central shorting pin for biasing simplification, which is integrated with a low-complicity reconfigurable feeding network consisting of relatively few PIN-diodes and biasing lines. Using such miniaturized and high-efficiency element, a polarization reconfigurable conformal array protype is designed, fabricated and experimentally characterized. Simulation and measurement results have shown that, the presented conformal array exhibits a 3-dB axial-ratio bandwidth of up to 1.3% at around 1.5 GHz, and -10-dB impedance bandwidth of 7.7% for two circular-polarization modes and 7.3% for two linear-polarization modes, respectively. Besides, the realized gain is over 14 dBi for all reconfigurable polarization modes over a relatively wide beam coverage range of 50° × 180°.

We investigate a novel downlink multicarrier direct-sequence code division multiple access (MC-DS-CDMA) resource allocation scheme in the context of low earth orbit (LEO) satellite-ground integrated networks (SGINs). In contrast to the existing MC-DS-CDMA works which mainly focus on delay-tolerant services in terrestrial networks, we consider the heterogeneous delay traffic and take the unique characteristics of LEO satellite systems into account. Specifically, we exploit the channel information, the delay requirement, the buffer state, and the visible time of users to construct a utility function and formulate the resource allocation optimization problem in presence of co-channel interferences (CCI) in LEO satellite-ground heterogeneous systems. We transform the original nonconvex optimization problem into two convex ones and use the Lagrange dual decomposition method to derive the solution. We also design an efficient algorithm dynamically scheduling subcarriers, codes, and transmission power of MC-DS-CDMA. The simulation results confirm the superiority of our work in terms of lower average delay and higher overall throughput.

This work tackles the joint optimization of the modulation order $L$ and transmit power $P$ of pulse-position modulation (PPM) in covert communications. Specifically, we consider two scenarios where $L$ used by the PPM at a transmitter Alice for communicating with a receiver Bob is known and unknown to the warden Willie, respectively. In the former scenario, our numerical examination obtained based on our analysis indicates that the optimal $L$ is generally 2, which is mainly due to the fact that Willie’s detection performance increases with $L$ when he knows it. Interestingly, in the latter scenario, we show that the optimal $L$ is 2 when the covertness constraint is strict, while it is 4 when the covertness constraint is relaxed. The main reason is that Willie’s detection performance does not depend on $L$ when he does not know it, and the bit error rate for $L=2$ is close to that for $L=4$ when the signal-to-noise ratio is extremely low caused by the strict covertness constraint, while $L=2$ requires less bandwidth and may achieve a higher bandwidth efficiency. Furthermore, we theoretically prove that the covert communication performance is better when Willie does not know $L$ relative to when he knows $L$ .

Satellite battery status prediction is crucial for ensuring the healthy operation of future satellite constellations. However, traditional telemetry-based methods, where satellite battery status is transmitted in real time to ground stations for processing, consume significant satellite bandwidth and introduce response delays. Advances in onboard computing and federated learning (FL) enable local model training and centralized parameter aggregation, reducing transmission overhead while leveraging distributed satellite data. Nevertheless, the unique orbital motion of satellites presents challenges for FL, primarily due to battery status heterogeneity arising from varying sunlight exposure. Limited onboard energy further necessitates balancing model performance with battery efficiency during local training. To tackle these issues, we propose ObsBattery—a position-aware FL framework that clusters satellites based on their orbital positions to improve model accuracy. ObsBattery employs a Dueling Deep Q-Network to dynamically determine satellite clustering and adapt local training rounds according to power availability, thereby reducing energy consumption during low-power phases. Evaluations on a real-world satellite battery dataset show that ObsBattery significantly improves both prediction accuracy and energy efficiency. Compared to a standard clustered FL approach, it reduces model MAE by 16% and energy consumption ratio by 6% under experimental conditions.