Beam hopping (BH) resource allocation in low earth orbit (LEO) satellite constellations poses a complex optimization challenge due to volatile traffic distributions and the need to balance multiple performance objectives. Therefore, we propose a two-stage optimization framework for inter-satellite load balancing and adaptive multi-objective scheduling. First, satellite–cell association is optimized using an improved simulated annealing (SA) algorithm to achieve inter-satellite load balancing. Then the BH pattern design is formulated as a multi-agent multi-objective Markov Decision Process (M3DP), aimed at enhancing system throughput while minimizing average service response delay. We propose an adaptive deep reinforcement learning (AdaDRL) algorithm with dynamic preference weights to balance multiple objectives based on the communication network state. Simulation results show that, against common benchmarks, our algorithm reduces inter-satellite load gap by 77.1% and consistently outperforms them in throughput and delay across diverse traffic intensities and uneven distribution scenarios.

With the rapid development of large-scale low-Earth orbit (LEO) satellite Internet, very LEO (VLEO) Earth observation constellations are increasingly using intersatellite links (LISLs) for cross-layer access to Internet satellites. It is an effective means to enhance data return throughput. When both observation and communication satellite constellations use lasers for networking, cross-layer access between the VLEO and LEO satellite networks requires reallocating lasers. This reallocation disrupts the original topology and impacts network performance. To maximize the collaborative operational efficiency of the double-layer network, we fundamentally analyze the relationship between topology links, throughput, and delay using graph and queuing theory. Innovatively, we discover the superiority of two axioms in addressing the cross-layer topology optimization problem, including the “Minimum Hop Count” and “Minimum Overlap Path.” Based on these axioms, a many-objective cross-layer topology optimization model is established that considers the hop count and the average link utilization frequency of both VLEO and LEO satellite networks. To reduce the reliance on centralized algorithms on global transmission demand, a local distributed interaction mechanism (LDIM) is proposed for cross-layer LISL establishment. An onboard novel distributed many-objective cross-layer topology optimization (NDMTO) algorithm is also introduced for VLEO satellites to manage access strategies. Finally, we use real data from Typhoon LEKIMA to create a multitask scenario and conduct packet-level simulations based on the Starlink and Dove constellations. The results indicate that, compared to existing benchmarks, the NDMTO algorithm improves data throughput by 26.73% and reduces the average transmission delay of emergency task data by 20.7%.

Given the crucial role of the earth observation satellites in numerous key applications, using Low Earth Orbit (LEO) satellite internet as intermediaries via Laser Inter-Satellite Links (LISLs) has emerged as a promising solution to help transmit substantial amounts of observation data to ground stations. For Very Low Earth Orbit (VLEO) observation satellites, optimizing the cross-layer topology between themselves and LEO communication satellites has become paramount. To mitigate existing centralized algorithms’ reliance on global data transfer requirement information, a Novel Distributed Interactive Mechanism (NDIM) for cross-layer LISL establishment is proposed. Here, the VLEO observation satellite decides its own access strategy based on local network information gleaned from three information exchanges with the LEO communication satellite. Within this mechanism, the cross-layer link optimization is performed via the formulation of a multi-objective topology optimization model, which considers the transmission requirements of observation satellites, load balancing amongst the communication satellite layer, and the transmission delay of emergency tasks. Based on this framework, we propose a Distributed Multi-objective cross-layer Topology Optimization (DMTO) algorithm. Our algorithm is novel in considering the remaining load of the intermediary communication satellites, and it allows observation satellites to decide on access plans on demand, given incoming data. Additionally, we used real data from Typhoon LEKIMA to establish a multi-task scenario and conducted packet-level simulations based on the Starlink and Dove constellations. The results indicate that, compared to the existing baseline, the DMTO algorithm increased the observation data throughput by 1.37% (326.95 GB) and reduced the average transmission delay of emergency task data by 4.20% (20.5 seconds).

As the demand for remote sensing increases, it is gradually becoming possible for remote sensing satellites to access large-scale communication constellations through laser inter-satellite links, enabling high-throughput data backhaul. This paper addresses the inter-layer topology planning problem using potential game theory for the first time, strategically framing it as a decision-making problem for remote sensing satellites to access communication satellites. Then, theoretical derivation confirms that the above problem is a potential game and verifies the existence of Nash equilibrium. Additionally, considering factors such as laser link establishment time, cross-layer link visibility time consumption rate, mission transmission delay, and communication satellite loading level, a potential game strategy selection probability update algorithm (PG) based on revenue contribution is proposed. Finally, the superior performance of the PG algorithm in terms of task completion, transmission delay, and transport layer network load is effectively demonstrated through two scenarios involving the Starlink&DOVE and GW&R-SAT constellations.

Given the crucial role of the earth observation satellites in numerous key applications, accessing the Low Earth Orbit (LEO) satellite Internet via Laser Inter-Satellite Links (LISLs) has emerged as a promising solution to help transmit substantial amounts of observation data to ground stations. For Very Low Earth Orbit (VLEO) observation satellites, optimizing the inter-layer topology between themselves and LEO communication satellites has become paramount. To mitigate existing centralized algorithms’ reliance on global data transfer requirement information, a Novel Distributed Interactive Mechanism (NDIM) for inter-layer LISLs establishment is proposed. Here, the VLEO observation satellite decides its own access strategy based on local network information gleaned from three information exchanges with the LEO communication satellite. Additionally, considering the transmission requirements of observation satellites and the remaining load of communication satellites, a multi-objective topology optimization model is formulated, leading to the proposal of a Distributed Multi-objective inter-layer Topology Optimization (DMTO) algorithm. Our algorithm is novel in considering the remaining loads of the intermediary communication satellites, and it allows for the transmission of streaming observation data. We perform simulations using real task data (from Typhoon LEKIMA), and our results show that DMTO improves over existing baselines in terms of observation data throughput and communication satellite load balancing.

The rapid deployment of Low Earth Orbit (LEO) satellites, driven by technological advancements and cost reductions, has led to the emergence of mega-constellations for satellite-based Internet services. Among them, the networking problem has become a significant area of research. Specifically, breaking away from the traditional Grid-Mesh + topology schemes has been a focal point. Although some related studies have been conducted, there are still three major challenges in optimizing the topology of laser inter-satellite links: the lack of theoretical derivation for satellite visibility, the need for comprehensive modeling goals, and the absence of network simulation verification. To address these challenges, we introduce the theory of visibility analysis of laser terminals in real scenarios and propose a theoretical model of topologically feasible solutions for integer linear programming. Furthermore, a mathematical model is developed that considers time delay, hop count, and link load as optimization objectives. The Many-objective Non-Grid-Mesh Topology Optimization (M-NGTO) algorithm, based on Non-dominated Sorting Genetic Algorithm III (NSGA-III), is then designed to effectively optimize the topology. The optimized Non-Grid-Mesh topology is validated through packet-level simulations on the Hypatia platform. Additionally, consistency analysis is performed to establish agreement between theory and simulation results. The results of simulations conducted on two megaLEO satellite Internet constellations, GW and Starlink, demonstrate that the performance of the Non-Grid-Mesh topology in the above three optimization objectives is approximately 39.11% better than the average of the Grid-Mesh + topology, confirming the effectiveness of the M-NGTO algorithm. The findings have significant implications for enhancing the communication performance and load balancing of satellite Internet systems.