International Journal of Satellite Communications and Networking最新文献

Deep space exploration has seen rapid advancements in recent years, accompanied by a surge in exploration equipment and service traffic. Delay/disruption-tolerant networks (DTNs) offer a robust communication architecture to support these missions by providing unified and efficient communication services. A critical network metric, the service payoff, reflects the value delivered by the network and is directly tied to the ability to meet quality of service (QoS) requirements for diverse service flows. However, in the deep-space DTN architecture, there is a lack of solutions for allocating corresponding network resources for different service flows to achieve QoS guarantees, making it challenging to improve network service payoff. Therefore, this paper introduces a fully centralized online resource allocation (FCORA) scheme to maximize the service payoff in deep space DTNs. By incorporating non-orthogonal multiple access (NOMA) technology, the scheme enhances spectrum utilization through joint optimization of bandwidth allocation, user clustering, and power control. To address the computational challenges of centralized allocation, we propose a semicentralized online resource allocation (SCORA) algorithm, which offloads fine-grained service flow control to edge nodes using parallel edge computing. Simulation results demonstrate that FCORA and SCORA significantly enhance service payoff, even under heavy traffic conditions. Furthermore, SCORA achieves low computational latency, meeting the real-time requirements for online resource allocation in deep space DTNs.

Dynamic channels, energy constraints, and the risk of warden detection pose significant challenges to securing covert communications in 6G ground station networks. To address these challenges, we propose a novel multi-agent reinforcement learning (MARL) framework that jointly leverages mobile relays and reconfigurable intelligent surfaces (RISs) to optimize relay trajectories, transmit power, and RIS phase shifts. Unlike traditional optimization-based and existing reinforcement learning approaches, the proposed scheme simultaneously maximizes covert throughput, minimizes energy consumption, and ensures robustness under imperfect channel state information. The optimization problem is solved using the multi-agent deep deterministic policy gradient (MADDPG) algorithm. Simulation results show that the proposed framework maintains a warden detection error probability above $�90�\%$ in over $�95�\%$ of time slots, while delivering covert transmission rate improvements of up to $�77�\%$ compared to static-RIS baselines and $�63�\%$ relative to greedy relay baselines. Moreover, the approach demonstrates resilience to channel estimation errors (e.g., the Gaussian perturbations with $�{\sigma }^2$ up to $�0.1$) and offers computational scalability, with inference time $�<�15��\mathrm{ms}$ per slot on GPU, making it suitable for real-time implementation in the next-generation satellite networks.

Satellite communication systems are important in global information exchange in areas such as military security, weather forecasting, and telecommunications. Advanced encryption standard (AES) is widely used in satellite communication due to its robustness and efficiency. However, conventional AES implementations focus primarily on encryption and decryption, which require further improvements in attack resilience, particularly against adversaries that use machine learning–based cryptanalysis and quantum threats. This paper examines the use of AES algorithms to protect data transmission over satellite communications. It introduces novel modifications to AES, incorporating machine unlearning as a defensive mechanism against attacks, chaotic injection for enhanced key unpredictability, and a quantum-inspired approach based on wavefunction collapse to mislead attackers. Machine unlearning dynamically alters cryptographic states upon intrusion detection, preventing key recovery. Chaotic injection is explored in the S-Box and key scheduling process to introduce controlled unpredictability without compromising reversibility. Additionally, a quantum circuit representation of AES is proposed, where an adversarial attack induces a wavefunction collapse, leading to incorrect but deterministic outputs. Performance evaluation under additive white Gaussian noise (AWGN) conditions demonstrates that the proposed AES achieves an average encryption throughput of 194 Mbps with only 3.2% additional computational overhead, whereas entropy analysis shows 7.85–7.92 bits/byte, with an improvement of 1.08% over standard AES and expanding the effective key space by approximately 6.6 times. Comparative security testing shows a 25% increase in resistance to differential attacks. Empirical machine learning attacks demonstrate a drop in classifier accuracy from approximately 92.3% for the baseline AES to 55.5% for the proposed scheme. These results confirm that the proposed modifications significantly strengthen AES while maintaining computational efficiency for real-world satellite communication scenarios.

Researching the satellite network link allocation problem to meet interstellar communication demands is an important subject. In this study, we first analyzed the characteristics of satellite networks and designed a topology processing mechanism based on finite state automata (FSA). Then, with the visibility of the satellite as the constraint and the communication delay performance of the inter-satellite link as the optimization goal, the link allocation problem of the navigation satellite network is modeled as a multi-objective optimization problem. Finally, for the established multi-objective optimization problem, an algorithm based on the combination of Q-learning and non-dominated genetic algorithm (NSGA-II) is proposed to solve the link allocation problem. The simulation results show that the network delay performance of the optimized link allocation obtained through the algorithm combining reinforcement learning and NSGA-II has been improved, and the link communication delay is better than the traditional multi-objective optimization algorithm. At the same time, the state duration of the FSA is reduced, which facilitates the acquisition of satellite links with good network delay performance. These research results show that the RL-NSGA-II algorithm based on the combination of Q-learning and non-dominated genetic algorithms has great potential in solving satellite network link allocation problems, providing better performance and effects for satellite networks that meet the needs of interstellar communications.

Interference mitigation remains a persistent challenge in satellite communication (SATCOM). Especially, a cost-effective and efficient approach is demanded to full-fill the 6G vision of ubiquitous coverage with low-cost satellites. To this aim, we advocate applying sidelobe cancellation (SLC) in combination with analog phased arrays for SATCOM uplink interference mitigation. In this paper, we conduct theoretical performance analysis of such SLC system by proposing an approximate signal-to-interference-plus-noise (SINR) model. Specifically, we provide a mathematical explanation for the relationship between SINR and auxiliary array gain, which is a pivotal inquiry in system design but remains inadequately addressed. Based on these novel findings, we also propose an approach to optimize the system performance via online control of the auxiliary array gain. The proposed models and methods are rigorously validated through extensive simulations.