This paper investigates a secure aerial reconfigurable intelligent surface (A-RIS) communication system, where user mobility, imperfect channel state information (CSI), and RIS phase errors induced by unmanned aerial vehicle (UAV) jitter significantly degrade performance. To address these challenges, we formulate a joint optimization problem for UAV trajectory, base station (BS) we propose abeamforming, and A-RIS beamforming to maximize the minimum secrecy energy efficiency (SEE), subject to constraints on user secrecy rates and UAV energy efficiency. To solve this highly non-convex problem, we propose a novel reinforcement learning framework termed IA-CSORL based on the twin-twin-delayed deep deterministic policy gradient (TTD3) architecture, which incorporates two novel modules. Specifically, we develop the phase-aware relativistic adaptive descent (PRAD) algorithm is proposed, which embeds the learning process into a conformal Hamiltonian system. By integrating gradient-based phase error correction and adaptive momentum adjustment, PRAD effectively counteracts phase noise and stabilizes training. Furthermore, we design an environment-state interactive attention (ESIA) mechanism to dynamically fuse UAV positioning and environmental features, enhancing state representation and deployment accuracy. Numerical results demonstrate that IA-CSORL significantly outperforms existing RL baselines in terms of both robustness and convergence performance. Moreover, IA-CSORL achieves superior beamforming accuracy under phase errors and CSI imperfections and provides a better trade-off between sum secrecy rate (SSR) and SEE, with performance gains becoming more significant as the number of RIS elements increases.

Low Earth Orbit (LEO) satellite communications have emerged as a promising paradigm for achieving ubiquitous coverage, driving the evolution of space-ground integrated networks (SGINs). The cell-free (CF) architecture has attracted significant attention in SGINs as the terrestrial segment for its potential to enhance capacity and connectivity. However, deploying CF necessitates numerous access points (APs), resulting in a prohibitive cost. To this end, we propose reconfigurable intelligent surface (RIS)- and simultaneous transmitting and reflecting (STAR)-RIS-assisted CF systems for SGINs, where part of the APs is replaced with cost-efficient RISs and STAR-RISs. Non-orthogonal multiple access (NOMA) is incorporated to improve connectivity under limited spectrum. We formulate transmit power minimization problems for both RIS- and STAR-RIS-assisted CF-NOMA in SGINs, jointly optimizing the active beamforming vectors of the satellite and APs, as well as the discrete passive beamforming (DPB) vectors of RISs/STAR-RISs. For the RIS-assisted scenario, a semi-definite programming (SDP)-based method is proposed to optimize the active beamforming vectors, while an enhanced integer linear programming (ILP) method is proposed to obtain the optimal DPB of RISs. To reduce complexity, we develop a low-complexity penalty-based SDP (PB-SDP) algorithm that achieves near-optimal DPB solutions. For the STAR-RIS-assisted scheme, both independent and coupled DPB for transmission and reflection are optimized alone with the active beamforming vectors. Numerical results demonstrate that: 1) The proposed systems outperform cell-based systems and heuristic optimization algorithms in terms of transmit power consumption; 2) The proposed PB-SDP algorithm achieves near-optimal performance with reduced complexity; 3) It is shown that DBP with 3 quantization bits achieves performance comparable to continuous passive beamforming (CPB) in both RIS- and STAR-RIS-assisted systems; 4) Also, it is shown that beyond a certain number of APs, further increasing the APs yields only limited transmit power consumption gains under a fixed total number of antennas.

The application of rate-splitting multiple access (RSMA) with robust interference management capabilities in multibeam satellite system has attracted significant attention. To ensure rate performance across all users while supporting diverse information services, including broadcast, multicast, and unicast transmissions, this paper proposes a transmission scheme based on hierarchical rate-splitting (HRS) aiming to achieve max-min fairness (MMF) in a multibeam satellite system. We also introduce an improved iterative optimization (IO) algorithm based on weighted minimum mean square error (WMMSE) to solve the non-convex minimum rate maximization problem. Moreover, both shortest distance and strongest channel gain user grouping methods are employed with flexible user number per group. Simulation results show that the proposed transmission scheme outperforms the conventional schemes.

With the rapid expansion of the Internet of Things (IoT) and the increasing unmanned devices, data transmission and sharing between unmanned agents in the Internet of Unmanned Agents (IUA) face significant challenges. Mobile Edge Computing (MEC), which extends computing power from the cloud to the network edge, has become a key technology for enabling efficient, low-latency communication services. However, with the surge in the number of terminal devices and the diversification of service requirements, traditional MEC deployment methods face challenges such as inflexible resource allocation and limited service coverage. This paper mainly researches an unmanned aerial vehicle(UAV)-assisted ground-to-air communication and computing system, constructs a multi-UAV network communication model and a computing model, and proposes a joint optimization problem of system task processing delay and energy consumption based on the total system delay and the energy consumption of the UAV. For the path planning and resource allocation problems of the multi-UAV network, an improved double-delay deep deterministic policy gradient algorithm is proposed to jointly optimize the trajectory planning, user association and task offloading strategies of the multi-agent UAV. Finally, the performance of the proposed algorithm is verified and analyzed through simulation experiments.

The communication among nodes in the space–air–sea integrated network (SASIN) relies on collaborative multihop transmission. Hence, effective routing techniques should be designed to optimize multiple indicators. Routing optimization for multihop is usually focused on optimizing a single metric. Moreover, designing effective routing strategies for multihop networks with SASIN is challenging as balancing multiple performance metrics can lead to conflicts. In this article, we propose near-Pareto multiobjective routing optimization for SASIN, which adopts multiobjective combinatorial optimization (MOCOP) to strike a tradeoff among multiple objectives. We establish the SASIN system model, including channel models of communication links between satellites, aircraft, and ships. Furthermore, we use multiobjective optimization methods to formulate objective functions of spectral efficiency, energy efficiency, and delay. We employ the multiobjective evolutionary algorithms (MOEAs) for approximating the set of the Pareto optimal solutions. An improved nondominated sorting genetic algorithm II (INSGA II) and an improved strength Pareto evolutionary algorithm II (ISPEA II) are proposed to generate approximations of the Pareto optimal set. We evaluated the MOCOP formulation, and the SASIN network topology was built based on real data and simulated data. The simulation results indicate that a set of beneficial tradeoff solutions can be obtained for providing flexible selection of communication connections by addressing the multiobjective routing problem formulated. The results demonstrate that the MOEAs utilized have the potential to find Pareto-optimal solutions for SASIN.

Space-ground integrated networks (SGINs) have attracted substantial research interests due to their wide area coverage capability, where spectrum sharing is employed between the satellite and terrestrial networks for improving the spectral efficiency (SE). We further improve the SE by conceiving a cell-free system in SGINs, where the full-duplex (FD) multi-antenna APs simultaneously provide downlink and uplink services at the same time and within the same frequency band. Furthermore, power domain (PD) non-orthogonal multiple access (NOMA) is employed as the multiple access (MA) technique in the cell-free system. To achieve a performance enhancement, the sum-rate maximization problem is formulated for jointly optimizing the power allocation factors (PAFs) of the NOMA downlink (DL), the uplink transmit power, and both the beamformer of the satellite and of the APs. Successive convex approximation (SCA) and semi-definite programming (SDP) are adopted to transform the resultant non-convex problem into an equivalent convex one. Our simulation results reveal that 1) our proposed system outperforms the well-known approaches (i.e., frequency division duplex (FDD) and small cell systems) in terms of its SE; 2) our proposed optimization algorithm significantly improves the networking performance; 3) the conceived SIC order design outperforms the fixed-order design at the same complexity.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) communication systems are investigated in its vicinity, where a STAR-RIS is deployed within a predefined region for establishing communication links for users. Both beamformer-based NOMA and cluster-based NOMA schemes are employed at the multi-antenna base station (BS). For each scheme, the STAR-RIS deployment location, the passive transmitting and reflecting beamforming (BF) of the STAR-RIS, and the active BF at the BS are jointly optimized for maximizing the weighted sum-rate (WSR) of users. To solve the resultant non-convex problems, an alternating optimization (AO) algorithm is proposed, where successive convex approximation (SCA) and semi-definite programming (SDP) methods are invoked for iteratively addressing the non-convexity of each sub-problem. Numerical results reveal that 1) the WSR performance can be significantly enhanced by optimizing the specific deployment location of the STAR-RIS; 2) both beamformer-based and cluster-based NOMA prefer asymmetric STAR-RIS deployment.

For the future sixth-generation (6G) wireless communication networks, improved metrics are expected to provide connectivity of massive devices, which brings new challenges for 6G networks extending to modern radio access for Internet of Things (IoT) applications. We consider a 6G enabled nonterrestrial network working in remote areas in this article, where an on-demand unmanned aerial vehicles (UAVs) provides the connectivity services. To improve the energy efficiency (EE), a method combining nonorthogonal multiple access (NOMA) and spatial modulation (SM) techniques is proposed and termed spatial NOMA (S-NOMA). Particularly, by employing multiple input multiple output (MIMO), SM only activates partial transmit antennas in per symbol interval, which can provide large data rate with less interantenna interference (IAI). Moreover, a power allocation optimization method subject to EE for S-NOMA scheme is proposed. Specifically, the antenna selection bits are determined by all users, which improves EE for all users instead of the selected one. Besides, the capacity expressions of S-NOMA are derived, then the EE performance of S-NOMA is analyzed. In addition, simulation results show that the proposed S-NOMA with energy-efficient power allocation performs better EE performance compared with the conventional NOMA.