This paper presents a traffic demand prediction method based on deep learning algorithms, aiming to address the dynamic traffic demands in satellite communication and enhance resource management efficiency. Integrating Seq2Seq and LSTM networks, the method improves prediction accuracy and applicability, especially for mobile terminals such as aviation and maritime ones. Unlike traditional approaches, it does not require extensive statistical data and can be generalized to real-world systems, providing stable long-term traffic demand predictions. This study utilizes real-world flight data mapped to corresponding satellite beams, allowing the precise prediction of beam-specific traffic demands. The results show that aggregating historical demand data for beams with similar trends achieves an average predictive Mean Squared Error (MSE) of 0.0007 and a maximum MSE fluctuation of 0.014, significantly outperforming predictions based on average values in terms of stability and accuracy. This novel solution for resource management in satellite communication ensures efficient and accurate long-term traffic demand predictions.

The increasing demand for satellite communication necessitates efficient resource management, especially as next-generation high-throughput satellites (HTSs) face challenges in optimizing user connections. This paper presents a novel method that integrates a discretely improved Particle Swarm Optimization (PSO) algorithm with a dynamic task management framework to enhance satellite resource allocation efficiency. The PSO algorithm is adapted for discrete selection problems to maximize a fitness function based on user priority, user preference, and capacity satisfaction, thus ensuring accurate user–satellite matching. A main loop function iteratively updates user data and connection statuses, thus achieving continuous optimization of satellite connections. Through simulation studies, we validated the effectiveness of this method under dynamically changing user demands, demonstrating that the Discrete PSO algorithm significantly outperforms Simulated Annealing (SA) and the Genetic Algorithm (GA) in user satisfaction, maintaining levels above 0.996 even under high demand. Additionally, it effectively prioritizes high-demand users, ensuring their satisfaction remains above 0.95. Overall, our method enhances the management of daily communication tasks, significantly improving service quality and user satisfaction.

With the expansion of the very-high-throughput satellite (VHTS) system, the uneven distribution of traffic demands in time and space has become increasingly significant and cannot be ignored. It is a significant challenge to efficiently and dynamically allocate scarce on-board resources to ensure capacity and demand matching. The advancement of flexible payload technology provides the possibility to overcome this challenge. However, computational complexity is increasing due to the unsynchronized resource adjustment and the time-varying demands of the VHTS system. Therefore, we propose a double-timescale bandwidth and power allocation (DT-BPA) scheme to effectively manage the available resources in the flexible payload architecture. We use a multi-agent deep reinforcement learning (MADRL) algorithm aiming to meet the time-varying traffic demands of each beam and improve resource utilization. The simulation results demonstrate that the proposed DT-BPA algorithm enhanced the matching degree of capacity and demand as well as reduced the system’s power consumption. Additionally, it can be trained offline and implemented online, providing a more cost-effective solution for the VHTS system.

In this article, we introduce and investigate a hybridization algorithm based on particle swarm optimization (PSO) and brainstorm optimization (BSO). The hybrid BSO–PSO (HBPSO) technique adopts PSO that is initialized by BSO within the starting iterations. The performance of HBPSO is significantly enhanced compared to single BSO or PSO when applied to high-dimensional optimization problems with local minima. The hybrid procedure is validated by showing appropriate convergence curves when applied to six benchmark functions. Guidelines regarding the selection of the inertial factor and switching iteration are investigated and presented accordingly. The proposed HBPSO is then validated using practical optimization tasks. It is demonstrated that HBPSO can outperform single PSO or BSO techniques in addressing representative antenna-related problems, including patch antenna circuit model extraction, conformal antenna array synthesis, and full-wave antenna design problems.