In low earth orbit (LEO) satellite communication systems, the limited energy supply capacity and the difficulty in obtaining channel state information (CSI) are the practical challenges. Motivated by this, we focus on the design of robust hybrid beamforming to maximize the energy efficiency of LEO satellite communication systems. Assuming that the LEO satellite transmitter adopts the massive multi-input multi-output (MIMO) technology, considering the CSI errors caused by propagation delay and Doppler shift, under the constraints of transmit power and quality of service (QoS), a robust energy-efficient hybrid beamforming scheme is proposed. Since that there are no explicit expressions for the ergodic user rate and the ergodic signal-to-interference-plus-noise ratio, the approximate values with closed-form expressions are adopted. Then, we invoke the semidefinite programming (SDP) algorithm to transform the nonconvex quadratic constrained quadratic programming (QCQP) problem equivalently, and an inner and outer nested iterative algorithm combining quadratic transformation fractional programming (QTFP) and concave convex process (CCCP) is utilized to transfer a nonconvex problem into a convex problem. Meanwhile, we adopt a penalty function algorithm to solve the rank-one constraint in semidefinite programming algorithm. Finally, we invoke the normal form distance minimization (NFDM) algorithm and the alternating optimization (AltOpt) algorithm to jointly solve the digital beamforming matrix and analog beamforming matrix in hybrid beamformer. Numerical results validate that our proposed robust approach significantly outperforms the conventional one.

Low earth orbit (LEO) satellite systems, an important part of the next generation of global communication systems, have the advantages of low transmission delay, low satellite cost and low launch cost. The construction of an LEO satellite network with global coverage has become the direction of future space network transmission development. Although extensive research has been conducted on the routing of LEO satellite networks, most papers focus on only space segment routing, with little attention paid to the route between the satellite and ground station. This paper introduces the transmission scenario of ground station switching with connected satellites and analyzes the problem of data packet loss caused by ground station and satellite communication link switching. Two optimization strategies based on static routing and dynamic routing are proposed as solutions to the problem of data packet loss, with software-simulated test results showing that both approaches can effectively avoid packet loss.

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are increasingly employed in mobile edge computing (MEC) systems to flexibly modify the signal transmission environment. This is achieved through the active manipulation of the wireless channel facilitated by the mobile deployment of UAVs and the intelligent reflection of signals by RISs. However, these technologies are subject to inherent limitations such as the restricted range of UAVs and limited RIS coverage, which hinder their broader application. The integration of UAVs and RISs into UAV–RIS schemes presents a promising approach to surmounting these limitations by leveraging the strengths of both technologies. Motivated by the above observations, we contemplate a novel UAV–RIS-aided MEC system, wherein UAV–RIS plays a pivotal role in facilitating communication between terrestrial vehicle users and MEC servers. To address this challenging non-convex problem, we propose an energy-constrained approach to maximize the system’s energy efficiency based on a double-deep Q-network (DDQN), which is employed to realize joint control of the UAVs, passive beamforming, and resource allocation for MEC. Numerical results demonstrate that the proposed optimization scheme significantly enhances the system efficiency of the UAV–RIS-aided time division multiple access (TDMA) network.

In this paper, we consider reconfigurable intelligent surface (RIS)-assisted integrated satellite high-altitude platform terrestrial networks (IS-HAP-TNs) that can improve network performance by exploiting the HAP stability and RIS reflection. Specifically, the reflector RIS is installed on the side of HAP to reflect signals from the multiple ground user equipment (UE) to the satellite. To aim at maximizing the system sum rate, we jointly optimize the transmit beamforming matrix at the ground UEs and RIS phase shift matrix. Due to the limitation of the unit modulus of the RIS reflective elements constraint, the combinatorial optimization problem is difficult to tackle effectively by traditional solving methods. Based on this, this paper studies the deep reinforcement learning (DRL) algorithm to achieve online decision making for this joint optimization problem. In addition, it is verified through simulation experiments that the proposed DRL algorithm outperforms the standard scheme in terms of system performance, execution time, and computing speed, making real-time decision making truly feasible.

Satellite communication has attracted great interests due to extensive coverage and no terrain restriction, which can make up for the shortcomings of terrestrial communication, especially in remote areas. Hence, integrated satellite-terrestrial relay network (ISTRN) is considered as a promising research direction. However, the shortage of spectrum resources is becoming the bottleneck of the future development of ISTRN because of the low utilization of spectrum. In this regard, non-orthogonal multiple access (NOMA) scheme and cognitive radio are considered as great ways to solve this problem. This paper investigates the performance of a NOMA-based ISTRN under spectrum sharing environment with primary interference. Particularly, the exact closed-form expressions of outage probability and ergodic capacity are derived to evaluate the system performance, where the interference constraint caused by the multiple primary users and the interference of primary transmitter have been considered. Furthermore, in order to reveal the impacts of key parameters on system performance at high signal-to-noise ratios efficiently, the asymptotic analysis of OP is also provided. After that, we obtain the relationship between system performance and the power allocation factor, the number of primary users, interference parameter or the channel environment, which will enlighten the research of NOMA-based cognitive ISTRN. At last, Monte Carlo results are provided to verify the effectiveness of our theoretical analysis.

Integrated satellite multiple terrestrial relay network (ISMTRN) is a new network architecture that combines satellite communication with terrestrial communication. It both utilizes the advantages of the two systems and overcomes their shortcomings. However, security issues inevitably arise in the ISMTRN resulting from the broad coverage of the satellite beams and the openness of wireless communication. One of the promising methods to achieve secure transmission is covert communication technology, which has been a hot discussion topic in recent years. In this paper, we investigate the performance of covert communication in the ISMTRN with partial relay selection. Particularly, when the satellite transmits its signal to the user, we consider the scenario that the selected relay opportunistically sends covert information to the destination. Furthermore, the closed-form error detection probability and average covert communication rate are derived. Finally, numerical simulation results are provided to reveal the impact of critical parameters on system covert performance.