A path-precomputation scheme is proposed to alleviate ATP delays in optical satellite networks. By forecasting requests on active links, it achieves up to 33.16% delay reduction, for 7.05× increase in runtime over conventional real-time computation.

With the continuously decreasing cost of launching satellites, low-Earth-orbit (LEO) optical satellite networks (OSNs) have emerged as an important research topic. By using laser communication, 10 Gbps laser inter-satellite links (LISLs) have been fully deployed, while 100 Gbps LISLs are rapidly advancing and are expected to become key components of future networks. With the expected growth in satellite-delivered service demand, OSNs will inevitably enter a hybrid stage in which 10 and 100 Gbps LISLs coexist. However, such coexistence may cause traffic bottlenecks of 10 Gbps LISLs and underutilization of 100 Gbps LISLs in LEO OSNs. From a network planning perspective, this paper focuses on developing efficient hybrid 10/100 Gbps LISL planning algorithms. We first propose a uniformly connected 100 Gbps LISL planning algorithm (UC-100GPA) to ensure the widespread deployment of 100 Gbps LISLs across the network. Based on this, a genetic algorithm for 100 Gbps LISL planning optimization (GA-100GPO) is proposed to further determine the sub-optimal deployment ratio of 100 Gbps LISLs in both the intra-orbit plane (intra-OP) and the inter-orbit plane (inter-OP). Simulation results show that UC-100GPA reduces the blocking ratio by 30.32% and 14.88% compared to deployments without 100 Gbps LISLs and with intra-OP 100 Gbps LISLs, respectively. When the traffic load is 800 Erlang, GA-100GPO achieves a blocking ratio of only 0.56% under a sub-optimal deployment ratio of 63.38% intra-OP and 36.62% inter-OP.

With the rapid development of 6G technologies, Low Earth Orbit (LEO) Optical Satellite Networks (OSNs) are becoming a crucial part of next-generation infrastructure. However, dynamical-changing topology poses challenges for path calculation, resulting in high computational overhead. To address this issue, this paper proposes a Graph Convolutional Network (GCN)-based path aggregation strategy that alleviates the computational burden associated with path calculation for service requests. The proposed method aggregates paths into a smaller set, mitigating the impact of changing topology. Simulation results show that the GCN-based strategy improves bandwidth utilization by 28.9% while reducing imbalanced link utilization by 15%, outperforming the traditional Dijkstra algorithm. The proposed strategy enhances network stability and resource utilization, offering a more efficient solution for path calculation in dynamic OSNs.

We propose a multi-layer constellation for optical satellite networks and a cross-layer path-aggregation routing strategy to establish persistent links. Our proposal reduces blocking probability (12.6%) and link utilization (10.2%) with respect to a state-of-the-art single-orbit solution.

Satellite optical networks combined with multi-beam technologies can be referred to as multi-beam satellite optical networks (MB-SONs). These networks are expected to play a crucial role in satellite Internet, potentially achieving Gigabit/s inter-satellite (IS) communication in the future. However, the growing demand for the satellite-to-mobile communications bring a challenge for making use of hybrid IS and satellite-to-ground (SG) transmission resources, which is worthy of studying. Given the different characteristics of IS links and SG links in terms of time windows and transmission capacities, existing solutions can hardly provide a suitable option for the optimal utilization of transmission capacity in MB-SONs. In this paper, we focus on the joint scheduling of optical wavelengths in IS and multiple beams in SG for the services sent from one ground station to the other ground station (which are referred to as end-to-end services). In response to the above-mentioned different characteristics of IS and SG links, we find that at least two constraints need to be followed in the joint scheduling. Also, the length of common time window will not exceed the minimum time window of IS and SG links on the path. The second one is that the volume of transmission capacity of a path depends on the length of CTW and the minimum bandwidth of IS and SG links, which should meet the service requirements. Considering these two constraints related to the length of CTW and the volume of transmission capacity, the main contributions of this paper can be concluded: i) defining the joint time slot allocation (JTSA) problem for multiple beams and wavelengths with different transmission capacities, ii) proposing an integer linear programming (ILP) model with object to minimize the number of time slots occupied by services, iii) designing the common time window for time slot assignment (CTW-TSA) algorithm as an option in practical implementations. The proposed ILP and CTW-TSA algorithm are evaluated by comparing the simulation results with the scheme using. The simulation of the CTW-TSA algorithm was compared to separate resource scheduling (SRS) without the store-and-forward function. The results showed a reduction of almost 0.201 in service blocking probability and an increase in average bandwidth utilization of about 0.159 for IS links and 0.164 for SG uplinks/downlinks.

Multi-Layer Low Earth Orbit Optical Satellite Networks (ML-LEO OSNs), a key component of 6G, offer large-scale deployment, global coverage, and highly dynamic topologies. However, continuously changing network structures, uneven spatio-temporal traffic demand, and limited inter-satellite links, bandwidth, and power hinder latency-sensitive services and degrade load balancing. Existing studies often overlook these multi-layer characteristics and rely on frequent on-demand route calculations, causing routing computation delays and suboptimal long-term load balancing. In this paper, we propose a Deep Reinforcement Learning-Based Service Path Prediction (DRL-SPP) Strategy. The network is divided into logical domains, and within each domain, a Mixed-Integer Nonlinear Programming (MINLP) model optimizes transmission delay while maintaining load balance. A Markov Decision Process (MDP) captures predictive routing patterns to anticipate future network states and enable proactive path planning, and a Deep Reinforcement Learning (DRL) algorithm based on Soft Actor Critic (SAC) adaptively optimizes resource allocation under dynamic and uncertain conditions. Simulation results show that the proposed approach significantly reduces end-to-end transmission delay and improves load balancing compared with existing schemes.

This paper investigates the service scheduling problem in low earth orbit (LEO) satellite networks comprising both electrical and optical switching satellites. The problem is formulated as a mixed integer linear programming (MILP) model, with the objective of jointly minimizing the total network energy consumption and the overall task completion time (TCT). Three routing strategies are considered, including least energy consumption (LEC), least delay (LD), and EnergyLatency Balancing (ELB) strategies. A simulated annealingbased heuristic algorithm is proposed for service scheduling in hybrid optical-electrical LEO satellite networks. Simulation results demonstrate that the proposed algorithm with ELB strategy can significantly reduce the total network energy consumption while maintaining low overall TCT, achieving performance that is close to that of the MILP model.

In the sixth-generation fixed network (F6G), network security becomes an important topic. Encryption is an effective method to prevent network attacks and realize network security. Quantum key distribution (QKD) is a promising technology to effectively address the challenge by providing secret keys due to the laws of quantum physics. New services such as high immersion experience and holographic have the characteristics of time-varying bandwidth and requirements. The introduction of optical service unit (OSU) technology makes it possible to provide the exact bandwidth used by the service. In optical transport networks, a lightpath needs to be established before service transmission, and will be removed after service transmission. Signaling is used for lightpath establishment, removal, and bandwidth adjustment. Data information transmitted in data layer and signaling information transmitted in control layer are highly vulnerable to cyberattacks, such as eavesdropping. The supply of bandwidth and key resources need to be optimized to achieve secure and stable service transmission in optical networks. Hence, how to realize bandwidth and key on demand (BKoD) provisioning for dynamic services is a key problem. To improve the flexibility of bandwidth and key resource allocation and utilization, a QKD-secured OSU-based optical transport network can be deployed. In this paper, a novel QKD-secured OSU-based optical transport network architecture is proposed and a service aware dynamic resource provisioning (SADRP) algorithm is proposed to realize BKoD. The proposed architecture uses the QKD technique to provide keys for both signaling information and data information for the first time. The proposed algorithm supplies resources according to the dynamic demand of bandwidth and key, so as to achieve the balance between dynamic demand and static resource utilization. Simulations results show that compared with the benchmark algorithm, the SADRP algorithm reduces blocking probability by 4.16%, reduces bandwidth resource utilization rate by 4.39%, reduces key resource utilization rate by 3.48%, and improves security rate by 4.17%.

Over years of space laser communication technology advances, satellite optical networks (SONs) have emerged as a pivotal component in 6 G networks. Satellite services are transmitted from the global view, undergoing transmission through SONs, and being downloaded to the targeted areas. However, the transmission capacity of satellites passing through the areas where users are concentrated may be insufficient to download services transmitted worldwide. This problem exists in various kinds of satellite networks and may cause a large amount of service congestion. In this paper, we propose a multi-downlink delivery routing selection (MDD-RS) strategy to study the total utilization of transmission capacity of SONs. We construct an integer linear programming (ILP) model to establish an optimal case study for minimal network capacity occupation. Also, we design an online option, MDD-RS heuristic algorithm, dynamically calculating path routes, considering bandwidth allocation and resource constraints. A comparative analysis against the conventional single-downlink scheme reveals superior performance of the MDD-RS heuristic algorithm, with a reduction in blocking probability of 0.129 and an improvement in bandwidth utilization of 0.032.

In the large-scale optical satellite network (LS-OSN), hundreds to thousands of low Earth orbit (LEO) satellites will be interconnected via laser links, offering global coverage characterized by high throughput and low latency. LS-OSNs present an attractive strategy to cultivate a comprehensively connected, intelligent world. However, the dynamic nature of the satellites, as they orbit the Earth, results in frequent changes in the LS-OSN topology. Thus, there is a pressing need for efficient routing algorithms that not only cater to massive traffic demands but also swiftly adapt to these constant topological changes. Traditional routing algorithms for services with specific bandwidth requirements often compromise on either computational speed or throughput efficiency. In response, this study introduces a routing scheme based on flow optimization and decomposition (FOND). This seeks to shorten the computation time while preserving optimal network throughput. Expanding upon the FOND scheme, we further devised two heuristic algorithms: the flow-based greedy path (FGP) and the flow-based greedy width (FGW). Simulation results from a 288-satellite constellation network indicate that both the FGP and FGW outpace contemporary methods in terms of the routing computation time while maintaining a consistent throughput equal to 100% of the network capacity. Notably, the FGP has exhibited an impressive capability, reducing the routing computation time to 0.23% compared to the baseline incremental-widest-path (IWP) algorithm, which operates on Dijkstra’s algorithm principles.

Satellite laser communication is gaining more and more attention, leading to rapid expansion of the scale of optical satellite networks. However, when the network scales up significantly, routing calculation time will become unmanageable, due to the increased time complexity of the routing algorithm resulting from the growing number of nodes and links. To address this issue, a topology-pruning based fast routing (TPFR) scheme for mega satellite optical networks is proposed in this paper. By establishing a representation of satellite optical networks and implementing topology pruning using regression tree method, the routing process is completed with reduced calculation time in small sub-graph. Simulation results show that TPFR can reduce routing calculation time by more than 45% compared to traditional routing algorithms.

The low-Earth-orbit optical satellite network (LOSN) is an attractive solution to realize the end-to-end service quality of large-scale services, and it is the basis of establishing the 6G mobile network with high throughput and high reliability. However, the current LOSN is practically unable to achieve such high throughput for global service access. First, the centralized deployment of ground gateways will cause a heavy traffic load in the space segment of the LOSN, which becomes the bottleneck constraining the further growth of network throughput. Second, the high-speed movement of LEO satellites will cause continuous movement of traffic load, which will affect the scalability of routing computing. In this paper, two routing algorithms are proposed to increase the throughput in the dynamic LOSN topology. The congestion-aware load balancing (CALB) algorithm is proposed to address the inter-satellite link congestion in the dynamic LOSN topology. Then, a load-balancing routing algorithm based on satellite–ground cooperation (SGC-LB) is proposed to further reduce the impact of the network bottleneck. To evaluate the performance of the proposed routing algorithm, extensive simulations were performed on a 288-satellite Walker-Star constellation with inter-satellite links. The service blockage rate and network bandwidth utilization rate are evaluated showing the effectiveness of the proposed routing algorithm. The network using the SGC-LB algorithm can accommodate 60.00%, 56.00%, 42.22%, and 33.33% more services than using the Shortest Path algorithm, Sway algorithm, CALB algorithm, and Anycast algorithm, respectively, with zero service congestion during the simulation. The SGC-LB also gains a 6.04%, 5.00%, 5.04%, and 0.77% higher network utilization rate than the Shortest Path, Sway, CALB, and Anycast algorithms, respectively.

Low earth orbit satellite laser communication has become an important part of communications due to its large capacity and low latency. The lifetime of the satellite mainly depends on the recharge and discharge cycles of the battery. The low earth orbit satellites frequently recharge under sunlight and discharge in the shadow, which leads satellites to age quickly. This paper studies the energy-efficient routing problem for satellite laser communication and builds the satellite ageing model. Based on the model, we propose an energy-efficient routing scheme based on the genetic algorithm. Compared with shortest path routing, the proposed method improves the satellite lifetime by about 300%, and the performances of the network are only slightly degraded, the blocking ratio increases by only 1.2%, and the service delay increases by 1.3 ms.

In this paper, a mobile-side access satellite selection algorithm based on regular coding is proposed. Simulation results show it can effectively reduce the connection latency and blocking ratio in walker-delta satellite optical networks.

Regarding the security of PONs, a secret-key reservation strategy is proposed to supply keys in failure duration for users to achieve security resilience. Simulation results show its effectiveness with various performances compared to the benchmark.

Low Earth orbit (LEO) satellite networks, which are composed of multiple inter-connected satellites, have become important infrastructure for future communications. Benefiting from the high bandwidth and anti-interference of satellite laser communication, optical satellite networks, in which satellite links are lasers, can provide global Internet services and have become a research trend. The orbit at a lower altitude has advantages such as low latency, low cost, and easy deployment in LEO optical satellite networks. Meanwhile, the movement of satellites is fast and thus will result in frequent changes for ground–satellite links. The conventional static routing strategy cannot perceive the network state; therefore, the static routing is inapplicable in the case of link failure or congestion. Dynamic routing can ensure the accuracy of the network connection by routing convergence. However, the routing table needs to be updated frequently because of the highly dynamic topology, resulting in the increase in signaling overhead. To compute routing paths accurately while reducing the update frequency of the routing table, this paper proposes a path computation model based on deep learning. By learning the mapping relation of previous services and the routing paths, the model can directly output the routing path according to the current service request. Using this method, the path computation tasks depend less on the frequently updated routing table. The simulation results show that the paths computed by the proposed method are almost the same as the paths computed by Dijkstra’s algorithm, the average accuracy rate is above 90%, and the highest accuracy rate can reach 98.8%. Compared with traditional path computation, the proposed method needs to collect a large amount of previous data for training, and the training time is about several hours.

We propose a quantum-key-distribution (QKD) key provisioning scheme by applying auxiliary graph for end-to-end security service in optical networks. Simulation demonstrates the good performance in terms of security level and key provisioning latency. © 2022 The Author(s)

The problem of satellite ground stations planning is of fundamental significance for optical satellite networks, especially in terms of network capacity and blocking ratio. In this paper, a new metric, i.e., uniformity, is introduced in optical satellite networks, and a satellite ground station planning based on the gravity model (GSP-GM) algorithm is proposed. Simulation results show that the GSP-GM algorithm can increase the number of ground-satellite links effectively and reduce about 6% blocking rate.

Targeting the inter-satellite link failure issues in the optical satellite networks, we design a shared path protection algorithm based on time window matching. Simulation results show the proposed algorithm can reduce the switching times of the working path and improve the sharing degree of the network compared to the benchmark algorithm.