Satellite mega-constellations (SMCs), comprising thousands of interconnected satellites, have emerged as critical infrastructure for 6G networks to achieve seamless global coverage. This paper addresses two fundamental challenges in SMC operation: 1) the inherent spatial-temporal traffic heterogeneity with continuously escalating demand, and 2) the diverging quality-of-service (QoS) requirements for diverse traffic types requiring robust end-to-end performance guarantees. To enhance resource utilization while ensuring service differentiation, we propose a novel dual-scale traffic management framework encompassing macroscopic network-level coordination and microscopic node-level adaptation. The macroscopic component formulates a multi-objective optimization framework that strategically allocates transmission paths by simultaneously minimizing inter-satellite link load disparities and end-to-end queuing delays. The microscopic component introduces an adaptive resource allocation mechanism that decomposes end-to-end QoS requirements into per-node service level agreements, employing federated learning based traffic prediction to enable dynamic resource pre-allocation based on real-time load conditions. This hybrid approach achieves load-aware resource provisioning that maximizes traffic completion rates while minimizing inefficient transmissions. Simulation results show our scheme outperforms the on-demand multi-objective optimization approach, improving traffic completion rates by 17.0-27.5% and resource utilization by 23.29-62.34% across varying loads, while reducing latency and enhancing fairness.

Satellite computing networks leverage the placement of computing resources on low-Earth orbit (LEO) satellite communication network to bring computing power closer to users, enabling robust and scalable solutions for emerging applications such as Internet of thing, edge computing and real-time analysis. However, with the explosion of multi-layer LEO mega-constellations(MLMCs), how to use the least number of computing nodes to achieve computing power resource coverage? This paper proposes a computing node deployment algorithm for MLMCs to construct satellite computing network with the minimum number of computing node. Specifically, we analyze the existence conditions of optimal computing node deployment, and derive the expression of the relationship between the number of computing nodes needed on the LEO, the multi-layer satellite network structure and the number of accessible hops of computing nodes. The expression can determine the minimum number of computing nodes needed to be placed on the MLMCs to realize optimal computing node deployment under a certain number of accessible hops. Then, according to the limitation of optimal computing node deployment, a multi-layer satellite network computing node deployment algorithm is proposed to determine the position of the computing resource in MLMCs, which can form a stable computing structure in satellite network. Finally, through simulation, the influence of network scale and computing node deployment on the number of required computing nodes and the effectiveness of signaling delay is analyzed. The simulation results show that this method can achieve better delay with the least number of computing nodes, balance the number of computing nodes and delay, and meet the specific network requirements.

Mega satellite constellations (MSCs) based on low Earth orbit (LEO) satellites and inter-satellite links (ISLs) have become increasingly important due to the seamless coverage and high throughput. Unfortunately, the communication components of satellites are susceptible to radiation-induced single event upsets (SEUs), which lead to the failure of ISLs and the decline in network throughput. In this paper, we study the impact of SEUs on network throughput and propose MSC design algorithms to enhance the throughput. To mitigate the impact of SEUs, each satellite is equipped with low-cost mitigation techniques, under which ISLs experience different levels of impairment. Furthermore, we derive the expressions of network throughput and observe the mismatch between the traffic pattern and the network topology. Based on the expressions, we develop the MSC design algorithm to address the gap for throughput enhancement. Simulation results validate the accuracy of the theoretical results, and demonstrate that the proposed algorithm can effectively enhance the network throughput by 8.42% compared to the classical topology under the impact of SEUs.

In data relay satellite networks (DRSNs), user satellite (US) data is transmitted to ground stations via geostationary (GEO) relay satellites (RSs). As the number of USs increases to enable real-time observation, the limited relay capacity becomes a critical bottleneck. On-board caching and processing at USs before transmission are promising approaches to alleviate relay pressure. However, constrained storage, computation and transmission (SCT) capacities pose significant challenges in meeting stringent delay and reliability requirements. This paper investigates the user capacity of a typical DRSN with integrated SCT processes, which is defined as the maximum number of USs that can be supported under both delay and reliability constraints. These constraints are quantified by delay violation probability (DVP) and data loss probability (DLP), whose expressions are difficult to derive directly due to the inherent coupling of SCT processes. To this end, tight upper bounds of DVP and DLP are derived based on a tandem queuing model with martingale-based analysis, and these bounds demonstrate exponential decay with increasing delay threshold and storage capacity. Based on these insights, a bi-level optimization problem is formulated and a two-step user capacity algorithm is proposed to efficiently obtain the user capacity under joint DVP and DLP constraints. The proposed analysis is conducted using representative DRSN parameters, and the numerical results show that the proposed methods can enhance user capacity by up to 38.2% and reduce computational complexity by around 90%. The results can provide guidance for future DRSN configuration, including satellites deployment and resources allocation.

Satellite networks with wide coverage are considered natural extensions to terrestrial networks for their long-distance end-to-end (E2E) service provisioning. However, the inherent topology dynamics of low earth orbit satellite networks and the uncertain network scales bring an inevitable requirement that resource chains for E2E service provisioning must be efficiently re-planned. Therefore, achieving highly adaptive resource management is of great significance in practical deployment applications. This paper first designs a regional resource management (RRM) mode and further formulates the RRM problem that can provide a unified decision space independent of the network scale. Subsequently, leveraging the RRM mode and deep reinforcement learning framework, we develop a topology feature-based dynamic and adaptive resource management algorithm to combat the varying network scales. The proposed algorithm successfully takes into account the fixed output dimension of the neural network and the changing resource chains for E2E service provisioning. The matched design of the service orientation information and phased reward function effectively improves the service performance of the algorithm under the RRM mode. The numerical results demonstrate that the proposed algorithm with the best convergence performance and fastest convergence rate significantly improves service performance for varying network scales, with gains over compared algorithms of more than 2.7%, 11.9%, and 10.2%, respectively.

The image task scheduling demand exhibits a thriving trend since China leverages heterogeneous low earth orbit (LEO) satellites to empower the earth observation field for Belt and Road Initiative (BRI) countries and regions. However, the high-dynamic mobility and intermittent inter-satellite links (ISLs) exacerbate communication, caching, and computing (3C) resources scarcity, while inefficient scheduling significantly deteriorates the successful transmission ratio (STR). To tackle this challenge, this paper optimizes the task scheduling strategy. To be specific, we first propose a dynamic resource mapping model (DRMM) that captures the dynamic characteristics of 3C resources through discontinuous ISLs and quantifies resources over continuous time. Based on the DRMM, we formulate an STR maximization problem under heterogeneous resource capacity constraints. A computing resources priority allocation algorithm (CRPAA) is designed to preferentially allocate onboard computing resources for task processing, thereby freeing up transmission and caching resources for additional tasks. Furthermore, the CRPAA leverages an incrementally searching slots range to reduce computational complexity and optimize the scheduling strategy, planning each task individually and clearing it promptly to prevent redundant scheduling while enhancing execution efficiency. The extensive simulation results validate that the proposed algorithm outperforms benchmark approaches.

Mega-constellation networks (MCNs) based on low Earth orbit (LEO) satellites have become increasingly important due to the high throughput and seamless coverage. However, due to single-event upsets (SEUs) caused by cosmic radiation, satellites will suffer failure, which deteriorates the network throughput. This paper aims to elucidate the relationship between network throughput and the impacts of SEUs. Taking into account the long-term impacts caused by SEUs, we present the availability of satellites based on the reliability theory. Furthermore, we model the effective data rate of inter-satellite links (ISLs) and find that the upper bound of network throughput $C \propto {\left( {\frac{{1 - {e^{ - \kappa {T_p}}}}}{{\kappa \left( {{T_p} + \gamma } \right)}}} \right)^2}\sqrt {{R_o}{R_h}} $, where κ denotes the SEU rate, and Tpis the scrubbing period for SEU mitigation. Roand Rhdenote the data rates of intra-plane ISLs and inter-plane ISLs, respectively. Consequently, the throughput decline caused by SEUs can be mitigated by adjusting the structure of MCNs. Guided by the throughput upper bound, we propose an optimal throughput constellation design algorithm (OTCDA) to enhance the network throughput considering the impact of SEUs. Experimental results illustrate that the proposed OTCDA can achieve the throughput that is only 6.49% lower than the upper bound.

The aerospace integrated networks (AINs), leveraging satellites and unmanned aerial vehicles (UAVs), offers a promising solution for large-scale Internet of Remote Things (IoRT), effectively ensuring information freshness, i.e., low Age of Information (AoI). However, in resource-constrained and dynamic AIN environment, a key challenge is how to achieve fresh data by efficiently resolving mission conflicts across multiple IoRT devices, which requires advanced scheduling design for collaborative UAVs monitoring and UAVs-satellites data transmission. In this paper, we first construct a mission scheduling framework for collaborative monitoring and transmission utilizing the wide coverage of low earth orbit (LEO) satellites and the mobility of UAVs. Then, by considering constraints such as mission conflicts, energy consumption, and motion characteristics, we characterize the relationship between UAVs trajectories and IoRT demands. Based on this, we propose a multi-agent deep reinforcement learning (MADRL) algorithm that jointly optimizes UAV trajectories and transmission scheduling. The algorithm incorporates a filter layer to optimize UAV cooperation, preventing redundant device IoRT selection and resolving mission conflicts. Simulation results indicate that the proposed algorithm can reduce 22.9% AoI compared to the benchmark.

Beam hopping (BH) has emerged as a critical solution for interference mitigation in mega-constellation satellite networks. Traditional ground-based centralized beam scheduling methods become infeasible in mega-constellations due to prohibitive computational complexity and inadequate responsiveness to bursty traffic demands. Given the non-convex and NP-hard nature of the multi-satellite BH optimization problem, we strategically decompose it into two subproblems. Hence the two-stage on-board BH method based on collaborative satellite clusters is proposed in this paper to address the challenges for efficient BH scheduling. The pre-activated cell selection stage is designed with a mechanism for dynamic updating of cell pre-activation probability to maximize the system throughput. In the cell-satellite matching stage, load balancing across satellites is achieved by minimizing inter-satellite load disparities. Simulation results show that the average throughput could be improved by over 10% compared to the baseline. Moreover, the difference in load between satellites is significantly reduced by 26.38%.

Low Earth orbit (LEO) mega-constellations equipped with laser inter-satellite links (LISLs) is an important part of future sixth generation (6G). However, how solar background noise affects LEO mega-constellations remains an open research topic. To this regard, this paper first derives the spatio-temporal distribution of affected LISLs in LEO mega-constellations at a specific moment, based on the characteristics of the impact, such as its location and duration. This distribution is then generalized to account for all moments during the Earth's rotation, considering the positional relationship between the LEO mega-constellations and the Sun. Additionally, we define two key metrics: the maximum number of affected LISLs (MNAL) and the affected duration ratio (ADR) to quantify the impact on the constellations. Several examples are presented to show that the MNAL decreases as the phase factor increases and increases with rising inclination. The ADR, on the other hand, increases with the phase factor, but initially increases and then decreases as the inclination rises. This work offers theoretical insights that can guide the design of future LEO mega-constellations.

Urban Air Mobility (UAM) systems are emerging as promising solutions to alleviate urban congestion, with path planning becoming a key focus area. Unlike ground transportation, UAM trajectory planning has to prioritize communication quality for accurate location tracking in constantly changing environments to ensure safety. Meanwhile, the UAM system, serving as an air taxi, requires adaptive planning to respond to real-time passenger requests, especially in ride-sharing scenarios. However, conventional trajectory planning strategies based on predefined routes lack the flexibility to meet unpredictable passenger ride demands. To address these challenges, this work first proposes constructing a radio map to evaluate communication quality. Building on this, we introduce a novel Multi-Source Hybrid Attention Reinforcement Learning (MSHA-RL) framework that integrates diverse data sources, balancing global and local insights for responsive, realtime path planning. Experimental results demonstrate that our approach enables communication-compliant trajectory planning, reducing travel time and enhancing operational efficiency.

Space-air-ground integrated networks (SAGINs) hold great promise in delivering ubiquitous aerial access, effectively meeting the demands for large-coverage on-demand services. Moreover, in 6G networks, the integration of Communication, Computation, Caching, and Control (4C) enables seamless connectivity, efficient data processing, optimized content delivery, and intelligent decision-making for next-generation services. However, various components like unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), satellites, and terrestrial networks each face distinct limitations. In this demo, we first showcase a SAGIN-4C-6G platform capable of establishing a high-capacity backhaul link to the core network while ensuring stable and continuous coverage. Experiments demonstrate that the proposed platform can deliver high-speed, on-demand air-to-ground (A2G) coverage with wireless backhaul, extending over an area of up to 100 km2. Beyond communication enhancement and optimization control, we also illustrate the potential for computation and caching services by deploying the proposed SAGIN platform.

Equipping satellites with computing resources to ensure efficient mission completion has become a pivotal trend in multi-layer satellite networks (MLSNs). The uneven spatial distribution of missions and computing resources across satellites necessitates advanced scheduling of communication and computing resources through satellite collaboration. However, the intricate interactions between communication and computing resources, the dynamic mission arrivals and computing resources, and the difficulty of collaboration across different layers in MLSNs present significant challenges for effective scheduling. This paper proposes a collaborative scheduling framework for low Earth orbit (LEO) and medium Earth orbit (MEO) satellites to support communication-computing integration in the MLSN. To adapt to network dynamics, we introduce a federated aggregation matrix and propose an intelligent MEO-LEO collaborative scheduling algorithm that optimizes the decision-making process under uncertain mission arrivals. Additionally, we design a distributed LEO-LEO collaborative scheduling algorithm that leverages the synergy between inter-satellite communication and computing resources to enhance scheduling capabilities and create communication-computing resource chains that meet mission requirements. Extensive simulations demonstrate that our proposed collaborative scheduling framework significantly enhances the scheduling capability of the MLSN.

The network topology is a typical element that affects the network capacity of low Earth orbit (LEO) mega-constellations. Considering the relative relationship between satellites, most mega-constellations with inclined orbits adopt the classic torus-like topology in which each satellite establishes four inter-satellite links (ISLs) with neighboring satellites. However, do we need so many ISLs to achieve the required capacity? In this paper, we provide an in-depth capacity analysis for quasi-torus topologies in which each satellite is connected by fewer than four ISLs. Based on the topological regularity and constellation scale, we first introduce the models of typical quasi-torus topologies. Considering that all the satellites are not symmetrical, we divide a quasi-torus topology into the same regions exhibiting axial symmetry. Combining the uniform traffic distribution and the symmetry of each region, we derive the closed-form formulae of network capacity by determining the maximum traffic load among ISLs. The formulae provide insights into the impact of ISL distribution and the constellation scale. Simulation results validate the theoretical analysis and show that the quasi-torus topology with fewer ISLs can achieve high capacity comparable to the torus-like topology. The theoretical analysis obtained is instrumental to the topology design of mega-constellations.

Satellite networks hold great promise for future 6G communications because of their benefits such as wide coverage and large capacity. Since the end-to-end (e2e) queuing delay is regarded as one key factor affecting the quality of service (QoS) in satellite networks, accurate delay prediction is a critically important topic. However, the delay prediction is complicated due to the irregular and time-varying features of inter-satellite links (ISLs) and satellite-ground links (SGLs), such as discontinuity and alternation. In this paper, we propose to establish the multi-node satellite-to-ground communication procedure as a multi-hop tandemly queuing model and present a heterogeneous heterogeneous multi-hop martingale model for queuing delay analysis. Due to the unique time-varying characteristics, we propose to model the SGL and ISL services as the stationary Markov processes using the Markov chain Monte Carlo approach. To match the intermittency and burstiness of traffic, the data arrival and service processes are handled using the Markov process. We propose to use a scaling factor for reflecting the heterogeneity of data processing capability, and then present a novel approach to ensure the stability condition requirement of the multi-hop system. Using the multi-hop heterogeneous martingale approach, the tight upper bounds of the delay and backlog in heterogeneous links are derived, and then precise delay prediction can be obtained. Finally, numerous simulations are conducted to demonstrate the effectiveness and accuracy of the proposed martingale method in analyzing the system delay and backlog when compared to the existing stochastic network calculus method.

Satellite-terrestrial networks (STNs) are emerging as a promising solution for provisioning comprehensive services, such as the Internet of Remote Things (IoRT) and remote sensing, within the realm of 6G wireless networks. Nonetheless, resource failures and the exigencies of diverse mission urgencies exacerbate the intricacies of resource scheduling in STNs, thus impeding the effective alignment of distinct mission requirements with dynamic resources. In light of these challenges, we first mathematically formulate the complex resource scheduling problem in STNs as a stochastic optimization paradigm, endeavoring to maximize the number of successfully accomplished missions. Subsequently, we conceptualize the resource evolution to delineate scheduling dynamics, encompassing potential contingencies of resource discontinuities. Next, we propose an innovative hierarchical deep learning-based mission-driven resource scheduling (HDL-MDRS) algorithm, aimed at optimizing resource collaboration and reconfiguration to amplify network performance within the dynamic ambits characterized by resource disruptions. The HDL-MDRS algorithm achieves a coarse-grained alignment of diverse mission requirements with multidimensional resources. It enhances overall mission fulfillment and network resource utilization efficiency through fine-grained collaboration and reconfiguration among satellites, both within and across different clusters. Notably, the simulation findings substantiate the effectiveness of the HDL-MDRS algorithm, effectively ensuring the requirements of different types of missions in case of the unforeseen resource failures, orchestrated through efficient resource collaboration and on-demand reconfiguration.

The inter-layer connection planning strategy of the multi-layer satellite mega constellations is a key technology to guarantee reliable and rapid transmission for various traffic demands. However, satellites deployed at different orbital heights cause more complicated layer-relative motions and frequently intermittent satellite connections. How to plan inter-layer links (ILLs) to guarantee high-throughput communication and provide reliable satellite relays is extremely challenging. In this paper, we leverage the satellite regular trajectories of each layer to design a hierarchical planning architecture, which consists of two phases, critical satellite selection and dynamic ILL planning. In the first phase, critical satellites are selected by traffic load and delay requirements, and they can connect with other layers to search for efficient relays. Considering the high dynamic motion between layers, we further propose an ILL planning problem between critical satellites and relays to maximize throughput and obtain robust ILLs. To tackle the proposed problem in large spatio-temporal scales, we propose a multi-agent learning-based ILL planning strategy, which can adjust switching directions based on the orbit relative position from critical satellites to relay satellites of various layers and obtain efficient ILLs. Simulation results illustrate that the optimal ILL number is less than 1/3 of its layer scale, and the proposed strategy can enhance throughput by 14.6%, and reduce the switching rate by 60.9% compared to state-of-the-art baseline algorithms.

Network control, including mobility management, resource management and e.t.c., plays a vital role in maintain the effectiveness and reliability of multi-layer low-earth orbit megaconstellations (MLMCs). One of the critical issues in satellite network control is how to choose the position of controller to achieve the optimal state of satellite network control structure with the least number of control nodes, so that any access satellite in the network can reach the control satellites in J hops at most and realize seamless coverage of the MLMCs by the control satellites without overlapping. This paper proposes a seamless coverage control structure to improve the temporal effectiveness of controlling signaling distribution in MLMCs. Specifically, this paper firstly analyzes the influence of the shape and size of satellite beam coverage on the network control structure. Based on the above analysis, the existence conditions of seamless network control structure are deduced through the design of satellite beam inclination angle, where any access satellite can reach the control satellites in J hops at most. Finally, taking access and mobility management function (AMF) as an example, we apply the placement of control units to the analysis of intersatellite handover strategy, and find that compared with AMF placed on ground stations and on middle-earth orbit satellites, the handover delay of the proposed scheme is reduced by 40.78% and 13.24%, respectively.

In a low earth orbit (LEO) mega-constellation, each satellite establishes four inter-satellite links (ISLs) with its neighboring satellites to provide high capacity. However, is it necessary to establish so many ISLs in every mega-constellation? This paper investigates redundant ISLs caused by the structure asymmetry. We illustrate the existence of redundant ISLs in a mega-constellation with$P$orbital planes and$S$satellites per plane when$P\neq S$. According to the relative relationship between$P$and$S$, we determine redundant ISLs in mega-constellations with different scales. Then we prove a proposition ensuring that the capacity experiences only a minor decrease after removing redundant ISLs. Guided by the proposition, we propose an asymmetric redundant ISL removal (ARIR) algorithm to remove the maximum number of redundant ISLs while achieving the expected capacity of the complete four- Islconnecting mode. Simulation results validate that our proposed approach can remove at most 25% ISLs and achieve the expected capacity in the classical Starlink constellation.

Mega satellite networks consisting of hybrid orbit satellites play an important role in the sixth generation (6G) wireless networks. Location management (LM) can ensure service continuity for mobile users and is one of the key technologies in mega satellite networks. Moving satellites (e.g., LEO, MEO)1 and users require repeated location updates, which creates the challenge problem of significant LM overhead. In this paper, we propose a novel dynamic hierarchical LM scheme based on the virtual attachment point (VAP). The approach utilizes MEO satellites to provide LM, and divides the direct association between the user and the satellite into two independent steps, including the association between the user and the VAP, and the association between the satellite and the VAP. With this mechanism, the user’s location no longer needs to be updated due to satellite movement. Besides, a dynamic adaptive location area (LA) scheme is proposed to update the user’s location. The scheme can reduce the update frequency of high-speed mobile users, keep the number of paging satellites controllable, and not increase drastically with the constellation scale, thus reducing the paging overhead. The simulation results demonstrate that the proposed LM technology solution can reduce the total LM overhead by at least 40.6%.

To deal with the challenges in mobility management of mega low-Earth-orbit (LEO) satellite constellations with long management delays and high signaling overheads, especially under the existing fixed and limited deployments of ground mobility management entities, the cooperative mobility management mode of medium-Earth-orbit (MEO) satellites and ground stations (GSs) has become an attractive tendency. In this paper, considering with the global non-uniform user distribution and constrained satellite storage resources, we propose a dynamic satellite-ground integrated mobility management strategy (DSG-MMS) to cope with the relative mobility among users, GSs, and satellites, which can dynamically decide the optimal GS/MEO management node with the minimal handover and migration delays. Specifically, the DSG-MMS optimization problem is modeled as distributed Markov decision processes, and a reinforcement learning (RL)-based management node selection method is presented to solve them, where each LEO satellite agent dynamically decides its own management node. To further implement the RL algorithm on the resource-limited LEO satellite agents, a novel tensor-based RL algorithm for DSG-MMS is proposed by means of the streamed low-rank tensor decomposition, where only the small-sized core tensor and factor matrices are kept and updated in the strategic optimization so as to realize low storage and computing overheads as well as fast convergence. We perform simulations for the proposed DSG-MMS with parameter configurations of actual Telesat, Kuiper, and Starlink satellite systems to evaluate the mobility management delay and overhead performances as well as the required satellite storage size for mobility management. Moreover, a case study and an architectural comparison are given to demonstrate the superiority of the proposed DSG-MMS than existing methods.

To meet the growing demand for joint data and energy transmission, research on wireless information and power transfer is being promoted. The resonant beam enabled information and power transfer (RBIPT), which supports long-distance, high-power, and wide-bandwidth information and power transfer, has sparked widespread interest. The point-to-multipoint RBIPT system shows great promise for enabling simultaneous RBIPT for multiple receivers. However, the enabling system architecture has not been well studied in the literature, which is holding back the system implementation. To solve this problem, we propose a time division multiplexing RBIPT (TDM-RBIPT) system for multiple access, and constract a novel metric to evaluate the information and power transfer performance. We explore the TDM-RBIPT mechanism and design the architectures of the transmitter and the receiver. For the information transfer performance evaluation, we take system latency and throughput into consideration. We propose to estimate the system delay with the martingale theory by modeling the dynamic data processing procedures as Markovian processes with the markov chain monte carlo (MCMC) method. To evaluate the power transmission performance, we consider the transmitter’s power costs and the receivers’ power benefits. Numerical results reveal the effectiveness of the proposed TDM-RBIPT system and validate the accuracy of the proposed metric.

Satellite telemetry, tracking, and command (TT &C) operations are critical to ensuring the normal operation of mega satellite networks. However, the distribution and number of ground stations are limited, making that the existing TT &C mission scheduling methods are difficult to satisfy the TT &C requirements in mega satellite networks, resulting in low TT &C mission completion rates. In this paper, we first construct a space-ground integrated distributed TT &C mission scheduling frame-work utilizing the broad coverage characteristics of geostationary earth orbit (GEO) satellites. Then, we explore the similarity in the TT &C mission scheduling process among adjacent ground stations or GEO satellites, that the TT &C missions execute within the visible time window between satellites and TT &C antennas. Building on this, we share similar features of mission scheduling between the stations through federated learning (FL) and capture the temporal features of TT &C mission scheduling using deep reinforcement learning (DRL) at each station. Therefore, we propose a federated deep reinforcement learning (FDRL) assisting TT &C mission scheduling algorithm in mega satellite networks to enhance TT &C mission completion rates. Finally, the effectiveness of the FDRL algorithm is verified through simulation experiments. Compare to the traditional space-ground integrated algorithm, the FDRL algorithm improves the TT&C mission completion rate by about 26.5 %.

Satellite telemetry, tracking, and command (TT&C) technology plays a critical role in maintaining stable operation and emergency scheduling in mega-satellite networks. However, due to the high-speed movement of satellites, the intermittent connection between the satellite and the ground station creates a dynamic and complex visibility period, leading to temporal resource utilization conflicts during the TT&C mission scheduling process. These conflicts can further exacerbate real-time response for emergency TT&C missions in large-scale satellite networks. To address this issue, we explore the time-aware dynamic characteristics of emergency TT&C missions and their impact on the scheduling time of routine missions. Then we propose an efficient method for reducing resource utilization conflicts based on Dynamic Priority and Minimum Disturbance (DPMD). Building on this method, we formulate the scheduling process of emergency TT&C missions as a reinforcement learning decision problem suitable for dynamic real-time environments. Additionally, we introduce the DRLETMS algorithm (Emergency TT&C Missions Scheduling Algorithm based on Deep Reinforcement Learning) to achieve faster mission scheduling strategies in highly dynamic environments. Simulations demonstrate the superior efficiency of our proposed algorithm in large-scale scenarios compared to typical heuristic algorithms. Furthermore, we examine the impact of various ground station distributions on the real-time TT&C performance of satellite networks under the same satellite scale, which can provide theoretical guidance for designing mega-satellite TT&C networks in the future.

As the number of connected and autonomous vehicles (CAVs) on road networks continues to increase, mixed transportation scenarios where CAVs and human-driven vehicles (HDVs) coexist are becoming more common. Establishing dedicated lanes (DLs) for CAVs is crucial for managing mixed traffic and improving road capacity. In this paper, we provide a theoretical analysis of the relationship between the market penetration rate (MPR) of CAVs and road capacity in both single-lane scenarios and multiple-lane scenarios with DLs. We derive a critical MPR for CAVs, at which they can be seamlessly accommodated within the DLs. Our numerical results show that CAVs should be prioritized to enter DLs first to optimize road capacity in mixed traffic. We also derive and validate the road capacity in multiple-lane scenarios and provide an optimal strategy for setting up DLs under varying MPRs to maximize road capacity. Overall, our study provides valuable insights into the significance of DLs for CAVs in mixed traffic and offers guidance on their implementation to improve road capacity.

As the type and volume of traffic increase in mega satellite constellations (MSCs), it is still a challenge to use limited resources to improve the traffic completion rate while providing differentiated services for different traffic. Rational allocation of resources depends on the satellite's accurate prediction of non-uniform traffic from the ground. Considering that centralized training methods require high resource occupancy of traffic data transmission and that numerous satellites need to adjust the prediction network frequently when the service area changes, we propose a federated learning-based traffic management strategy (FLTM). We first classify the traffic according to performance requirements such as delay and connectivity, and create network slices to provide differentiated services. Then we train a unified prediction network between multiple service areas in a distributed manner based on federated learning, reducing the frequency of satellites adjusting the prediction network while sharing the traffic information of these areas. To capture spatial and temporal features of traffic, we also propose a prediction framework combining the densely connected convolutional neural network with the Long Short-Term Memory network. Finally, we adopt traffic prediction results to more reasonably allocate resources among slices. Real traffic data verify the accuracy of the proposed prediction framework and simulation results validate that FLTM can provide differentiated services for different traffic and improve the traffic completion rate of MSCs.

The expansion of satellite applications induces the formation of the multi-domain satellite system (MDSS) containing multiple domains with specific applications such as earth resource remote sensing and the Internet of remote things. Resource management is pivotal in enhancing the scheduling capability of the MDSS. However, this is challenging since the dynamic buffer space and communication opportunity, as well as the uncertain data traffic, exacerbate the difficulty of matching satellite resources with data traffic. Moreover, the coexistence of resource competition and collaboration across domains aggravates the dilemma of cross-domain collaboration. In this paper, we propose a hierarchical cross-domain collaborative resource management framework that can flexibly allocate the mission data through local intra-domain and global cross-domain scheduling. Then, to match the uncertain demands of missions with dynamic and limited resources, we propose a multi-agent reinforcement learning-based resource management method to guide collaboration for multi-satellite data carry-forward in a domain. Further, considering resource competition and collaboration in MDSS, we propose a domain-satellite nested matching game data scheduling algorithm to achieve pair-wise stable collaboration of cross-domain satellites. The simulation results indicate that the proposed algorithm improves the amount of offloaded data by 64.4% and 12.7% compared to the non-collaborative and the non-cross-domain schemes, respectively.

The new generation satellite network is a comprehensive service system that can provide communication, observation, navigation, and other functions. Typically, a system with a specific service function is defined as a domain and the supply and demand relationship of resources, such as communication, storage, and energy resources, are unbalanced among domains. Therefore, it is nontrivial to accurately characterize the cross-domain resource state and coordinate the data transmission policy of interrelated satellites from different domains to make full use of the resources in each domain aiming at improving the resource utilization ratio (RUR) of the whole network. To this end, this paper investigates the cross-domain resource scheduling (CDRS) problem in satellite networks aiming at maximizing the total amount of downloaded transmitted data. We start with the orbit motion law of satellites and construct the satellite orbit feature matrix of each domain to design a hierarchical sparse resource representation (HSRR) scheme to accurately characterize the resource state of each domain in real-time with low complexity. Further, based on the HSRR, we develop a cross-domain dynamic multi-resource scheduling algorithm to solve the CDRS problem by introducing the advantage factor and policy-oriented hyper-parameter. The algorithm can make full use of the resources in each domain to achieve efficient CDRS by dynamically adjusting the data transmission policy of satellites among domains. Simulation results show that the greater the service demand and resource difference among domains, the more significant the performance improvement brought by CDRS, and compared with the existing algorithms, the RUR and resource utilization efficiency have been significantly improved.

The number of satellites in the current low-Earth-orbit (LEO) satellite networks continues to grow to form a mega LEO satellite constellation (MLSC). This large-scale net-working effectively improves network coverage and capacity. However, denser satellite deployment brings more severe chal-lenges to satellite handovers, such as frequent handovers, which exponentially reduce the system performance (e.g. probability of service success (PSS)) when considering inherent handover failures. To ensure service continuity, this paper focuses on the relationship between the constellation scale and handover times under seamless coverage. Specifically, we first conduct spatial geometric analysis and probabilistic analysis to derive three new conditions for MLSC seamless coverage. Then, we analyze the tradeoff relationships between the handover times and satellite coverage duration with the given constellation scale. Furthermore, we construct a mathematical relationship between the constellation scale, handover times, and PSS, indicating the tradeoff between constellation scale and system performance. The analysis effectively guides to design or adjust the constellation scale, satellite altitude, satellite coverage angle, and handover strategy according to the requirements of system performance, which has important theoretical value for MLSC system design and future research.

Satellite-terrestrial networks (STNs) consisting of satellite segment and ground segment have been regarded as a desirable solution for 6G. Efficient cooperative resource scheduling strategies, which cover the cooperation in satellite segment for data relay and the cooperation between satellite segment and ground segment for data downloading, play a pivotal role in enhancing the system performance in STNs. Since the dynamic channel condition and energy feeding greatly influence the network status, cooperative resource scheduling should be adaptive to the future environmental fluctuation. In this paper, we model the cooperative resource scheduling problem in STNs as a resource limited Markov Decision Process (MDP). Considering the fact that satellites are unaware of future environmental status, the traditional static optimization solution is infeasible. Therefore, we propose a Deep Reinforcement Learning (DRL) based Cooperative Store-and-Relay Resource Scheduling Algorithm (CSR-RSA), where inter-satellite links are utilized to coordinate with intermittent satellite-ground links for improving the transmission performance of the network. By exploiting the proposed CSR-RSA, the well-trained neural networks can be obtained to generate the adaptive and cooperative resource scheduling strategy without the knowledge of future environmental status. Simulation results verify the effectiveness of the proposed algorithm compared with traditional algorithms.

As the spaceborne resources are heterogeneous and the satellite network topology changes constantly, various time-varying derivative graphs are designed to represent the data acquisition and delivery process in satellite-terrestrial integrated networks (STNs). However, the time complexity of resource allocation approaches based on assorted time-varying graphs is remaining obstinately exponential. Derived from the satellite vertical coverage feature, we design a more general relationship to involve a group of nodes in a hyperedge rather than the bilateral relationship between two nodes. In this paper, we propose a time-expanded hypergraph (TEH) to contract the adjacency matrix of the network topology. Based on the proposed TEH, the problem is formulated to minimize the consumption of the communication resource in the resource-limited STN while completing the same number of tasks. Since the problem is intractable by exhaustive search, we further propose a hybrid hyperedge and Lagrangian relaxation algorithm to perform optimal resource allocation through an oriented search for the feasible hyperedges to reduce the scale of searching. The simulation results validate that the proposed algorithm can effectively complete the tasks with lower time complexity.

Low Earth orbit (LEO) satellite constellations have become a promising architecture to integrate with terrestrial networks for facilitating communications and Internet of Remote Things (IoRT) services through gateways. Nevertheless, different gateway locations may have various channel conditions and service demands due to differentiated atmospheric conditions, populations, and number of terminal devices required by IoRT services in different areas. Besides, the gateway placement scheme further affects the service coverage performance and the access performance of the network to service demands. Therefore, gateway placement plays a pivotal role in improving network capabilities in the integrated system of LEO satellites and terrestrial networks (ISoLS-TNs). Motivated by the aforementioned facts, in this article, we first formulate the gateway placement problem in ISoLS-TNs as a multiobjective optimization problem to maximize the total revenue of service data demand within coverage while minimizing the average access distance and the number of deployed gateways. In order to enhance network resource utilization and assure local information confidentiality, a distributed resource allocation (DRA) mechanism based on the alternating direction method of multipliers (ADMMs) algorithm is designed to calculate the total revenue of service data demand within coverage. Furthermore, we propose a genetic-based gateway placement algorithm with the ADMM for DRA. Finally, through massive simulations based on real data, we validate the effectiveness of the proposed algorithm in improving resource utilization and coverage performance of the network. In addition, the results also bring lights on the relationship between service data demand distribution and gateway location preference.

Satellite networks (SNs) are heterogeneous networks composed of typical functional domains, such as communication domains, observation domains, etc. Since the resources are independent among domains, and are highly dynamic with the movement of satellites, it is extremely difficult to capture the potential interaction relationship among various resources, even less to realize the coordinated scheduling of cross-domain resources in large-scale SNs. Motivated by these factors, we firstly propose a multi-aspect expanded hypergraph (MAEH) to accurately depict the Spatio-temporal features of resources in various domains as well as functional property, which is presented by different aspects. Particularly, the “aspect” can involve a group of resources with a similar feature in a hyperedge rather than a bilateral relationship between two individuals, thus the MAEH can dramatically reduce the redundant connections. By exploiting the MAEH, we model the multi-domain resource allocation problem in the form of mixed-integer linear programming to maximize the completed tasks. Through the topological nested characteristic of the MAEH, we propose a two-stage scheme to accomplish the resource allocation rapidly with lower computational complexity and to improve the resource utilization ratio efficiently. Simulation results validate that compared with the optimal performance, the executive time of the proposed scheme is average fivefold less under 4% extra communication resource cost. Besides, the resource utilization ratio improves over 15% by cross-domain collaboration.

The network control plays a vital role in the mega satellite constellation (MSC) to coordinate massive network nodes to ensure the effectiveness and reliability of operations and services for future space wireless communications networks. One of the critical issues in satellite network control is how to design an optimal network control structure (ONCS) by configuring the least number of controllers to achieve efficient control interaction within a limited number of hops. Considering the wide coverage, rising capacity, and no geographical constraints of space platforms, this paper contributes to designing the ONCS by constructing an optimal space control network (SCN) to improve the temporal effectiveness of network control. Specifically, we formulate the optimal SCN construction problem from the perspective of satellite coverage factors, and apply geometric topology analysis to derive both the conditions for constructing the optimal SCN and the formulaic conclusions for SCN and MSC configurations (i.e., scale and structure). From numerical results, we investigate the tradeoff between network scale, the number of controllers, and control delays in several satellite network control scenarios, to provide guidelines for the MSC control. We also design the optimal SCN for an existing MSC system to demonstrate the effectiveness of the proposed ONCS.

With the growth of global communication service demand, constructing large-scale satellite networks has become the future development trend for improved system performance. However, due to the high-speed orbit motion of satellites, the connection relationship of network topology (CRNT) is complex and changeable. This phenomenon is particularly pronounced in large-scale satellite networks and the existing representation schemes of CRNT for large-scale satellite networks have high space complexity. Therefore, we explore the sparse characterization of the inter-satellite visibility matrix and propose an integrated sparse space-time resource representation (ISST-RR) scheme to efficiently characterize the satellite network communication resources with low complexity from the dimension of time and space. On the basis of the proposed ISST-RR scheme, we further propose a multi-agent reinforcement learning with sparse representation based resource scheduling (MARLSR-RS) algorithm to obtain the optimal resource scheduling policy. Simulations demonstrate the efficiency of the proposed MARLSR-RS algorithm in terms of communication resource utilization. In addition, we investigate the impact of several typical netwrok parameters, e.g., transmission rate of observation satellites on network performance, which can provide a theoretical guidance for system design.

The aerial access network (AAN) is a significant issue in the sixth generation (6G) technologies. In this work, we focus on the terrestrial data collection and transmission by AAN. In detail, high altitude platforms (HAPs) are considered as the aerial access devices and low earth orbit (LEO) satellites assist the data collected by HAPs to complete transmission. To deal with the intractable dynamic topology of AAN, the time expanding graph (TEG) is employed to represent the multiple resources and depict the data flow transmission process. Based on TEG, we aim to maximize the total data received at the ground data processing center, considering the multiple resource restrictions of HAPs and LEO satellites, as well as the flow conservation constraints in TEG. The problem is in the form of mixed integer programming, and it is intractable to obtain the optimal solution, especially in large-scale AAN. To alleviate the intractability, we propose the Benders decomposition based algorithm to obtain the optimal solution within an acceptable time complexity. Simulations are conducted and numerical results verify the effectiveness and efficiency of the proposed algorithm.

An efficient resource allocation algorithm plays a pivotal role in the performance improvement of space information networks (SIN). The dynamic of resources and mission requirements in the network has a great influence on resource allocation. Guiding rapid satellite resource allocation to adapt to dynamic changes of the network by discovering the similarity in the structure of changing missions is a key technology to improve SIN performance. In this paper, we firstly formulate satellite resource allocation as a problem aiming to maximize the total network benefits. Then, we propose a satellite resource allocation algorithm based on Hopfield to solve resource allocation problems in SIN. To accommodate the dynamics of satellite network missions and quickly solve resource allocation problems, we further propose a satellite resource allocation algorithm based on transfer learning to learn the node selection strategy in the Hopfield network. Specifically, we use a small number of additional training samples to better adapt to the changes in mission structure. Simulation results validate that the proposed method can achieve high performance while reducing computational complexity.

Non-terrestrial network (NTN) communication is included in the 3GPP standard because of its excellent features such as resistance to ground physical attacks and wide coverage. Since the available power and storage resources are limited on high throughput satellites (HTSs), optimizing the resource allocation can greatly improve the performance of the HTS based communication system. Notably, weather and other factors make the channel status between the satellite and the terrestrial base stations constantly change. In this paper, we first exploit the model-free feature of reinforcement learning to formulate the aforementioned dynamic and unpredictable channel conditions into the power allocation problem in HTS systems. Due to the complexity of environment state and power allocation, a deep neural network is introduced to replace the Q table, and a deep reinforcement learning framework is built. Furthermore, a power allocation algorithm based on a deep reinforcement learning framework is presented. Finally, the simulation results show that the proposed algorithm is superior to existing algorithms in terms of the long-term system throughput performance, and has better adaptability to dynamic channel environment, thereby improving network performance.

With the growing demand for communications in remote and dispersed areas, Internet-of-Remote Things (IoRT) networks with joint unmanned aerial vehicle (UAV) access and geostationary orbit (GEO) satellite backhaul hold great promise to provide sufficient access services to Internet-of-Things (IoT) users and devices. As the fundamental of the performance optimization of IoRT networks, the performance analysis sheds light on the relationship between the network performance (i.e., backlog, delay, and throughput) and access scale (i.e., the numbers of UAVs and UAV users). Aiming at the challenges brought by the complex network structure (i.e., two-level queuing network along with the converged traffic), we introduce the stochastic network calculus-based min-plus convolution and the leftover service to mathematically describe the complex structure. For the analytical challenges of the continuous-time arrival process and heterogeneous two-level link capacities, we innovatively prove their supermartingale features and further derive the closed-form expressions of the network backlog and delay bounds based on the martingale theory. To pursue higher throughput while guaranteeing delay performance, we formulate a mixed-integer optimization problem of the access scale that contains a nondifferentiable variable derived from a transcendental equation. For the tractability, we propose a three-directional iterative (TDI) algorithm to search the optimal solution of the optimization problem. Simulation results verify the tightness of our performance bounds in contrast to the standard bound and the effectiveness of the proposed algorithm.

Space-air-ground networks play important roles in both fifth generation (5G) and sixth generation (6G) techniques. Low earth orbit (LEO) satellites and high altitude platforms (HAPs) are key components in space-air-ground networks to provide access services for the massive mobile and Internet of Things (IoT) users, especially in remote areas short of ground base station coverage. LEO satellite networks provide global coverage, while HAPs provide terrestrial users with closer, stable massive access service. In this work, we consider the cooperation of LEO satellites and HAPs for the massive access and data backhaul of remote area users. The problem is formulated to maximize the revenue in LEO satellites, which is in the form of mixed integer nonlinear programming. Since finding the optimal solution by exhaustive search is extremely complicated with a large scale of network, we propose a satellite-oriented restricted three-sided matching algorithm to deal with the matching among users, HAPs, and satellites. Furthermore, to tackle the dynamic connections between satellites and HAPs caused by the periodic motion of satellites, we present a two-tier matching algorithm, composed of the Gale-Shapley-based matching algorithm between users and HAPs, and the random path to pairwise-stable matching algorithm between HAPs and satellites. Numerical results show the effectiveness of the proposed algorithms.

Low earth orbit (LEO) satellite networks will play important roles in the sixth generation (6G) communication system. Software defined network technique is a novel approach introduced to the LEO satellite networks to improve the resource flexibility and efficiency, forming the software defined LEO satellite networks (SDLSNs). How to efficiently allocate the resources of SDLSN to provide services for the terrestrial users is a key issue. Hence, in this work, we explore the service provision for SDLSN via virtual network functions (VNFs) orchestration on the software defined time-evolving graph. In view of the scarce, intermittent and unstable satellite-to-satellite (S2S) links, the problem is formulated to minimize the S2S resource consumption while satisfying the terrestrial tasks, which is in the form of integer linear programming. Since the problem is intractable by exhaustive search, we design a branch-and-price algorithm based on the coupling of Dantzig-Wolfe decomposition, column generation, and branch-and-bound to efficiently acquire the optimal solution. Further, to obtain a faster solution for practical usage, we further design an approximation algorithm for the subproblem and leverage the beam search to accelerate the pruning for the search tree. Finally, extensive simulations are conducted and the numerical results validate the effectiveness of the proposed schemes.

Satellite networks have been regarded as a promising architecture for supporting the Internet of remote things (IoRT) due to their advantages of wide coverage and high communication capacity in remote areas, which further promotes the development of the satellites for IoRT networks (SIoRTNs). The effectiveness of multi-dimensional resource collaboration has significant impacts on the IoRT data downloading performance. However, the environment ’ s dynamics, e.g., channel conditions and solar infeed process, are unknown in practical scenarios, which poses daunting challenges in making efficient utilization of multi-dimensional resources. Motivated by this fact, we model the joint resource scheduling and IoRT data scheduling problem with the aim of maximizing the amount of the IoRT data of the overall network by applying the model-free reinforcement learning framework. To overcome the limitations of traditional reinforcement learning algorithms, we propose several feature functions by investigating the natural attributes of the multi-dimensional resources of the SIoRTNs, and further exploit the concept of function approximation to approximate the expected downloaded IoRT data given the network state. Furthermore, we propose a state-action-reward-state-action (SARSA) based actor-critic reinforcement learning (SACRL) resource allocation strategy to achieve the optimal resource allocation and IoRT data scheduling with casual information at LEO satellites. Simulations validate the convergence property and the effectiveness of the proposed SACRL algorithm in terms of the amount of the downloaded IoRT data. Particularly, we investigate the impact of typical network parameters on network performance to further provide guidance for future SIoRTN system design.

The satellite data relay networks (SDRNs) hold great promise in 6G communications for the timely offloading of the global traffic. Since the delay performance is regarded as one of the most important metrics reflecting the offloading efficiency, studying its relationship with network parameters becomes really essential to the development and application of the SDRN. However, the complex data offloading process and heterogeneity of traffic arrivals and transmission links pose many challenges to the stochastic delay analysis. To accurately model the data offloading process in SDRNs, we build a series-parallel queuing model with through and cross traffic while considering the propagation delay. On this basis, we respectively propose a propagation delay embedded min-plus convolution method based on stochastic network calculus and a Markov chain method based on Monte Carlo to depict the leftover services of the heterogeneous links received by the per-flow traffic in an aggregate. To eliminate the impacts of the heterogeneity, we uniformly characterize the arrivals and leftover services by their moment generating functions (MGFs) which contain the full moment information, and shield the heterogeneity by deriving the envelopes of the arrivals and leftover services with the help of MGFs, Chernoff bound and union bound. Then, in the light of the geometric relationship between the envelopes of the arrivals and leftover services, we analyze the upper bounds of the stochastic delay, which provides the guidance to the network configuration. Eventually, simulation results verify the effectiveness of the theoretical analysis and further reveal maximum four times the delay difference between the heterogeneous links influenced by traffic type, burstiness, and access number.

Satellite Data Relay Networks (SDRNs) play an important role in the data relay from User Satellites (USs) to ground stations by Tracking Data Relay Satellites (TDRSs). For better exploitation of SDRNs, the development of the systematic model and accurate system analysis is essential. To describe the end-to-end data transmission in SDRNs, we construct an MMOO/MMSP/1/K-G/G/1 tandem queuing model where the two parts depict the traffic arrival and transmission service of USs and TDRSs, respectively. Because the active and inactive periods of the data transmission are determined by the visibility between two satellites, classical buffer state based vacation policies become imprecise. Moreover, these two kinds of periods appear alternatively and their duration varies over time so that it is hard to model such intermittent transmission by existing service models. To overcome these difficulties, we propose a Markov Chain Monte Carlo based Markov Modulated Service Process (MMSP) which can tightly match the distributions of the active and inactive periods. In this process, we propose two algorithms to calculate the service state transition probability and the number of the sub-states in each service state, respectively, which guarantees the alternative transition between the active and inactive states as well as the sojourn time spent in each state. For the quality of service analysis, we find the different features of the queue variation under different arrival and service rate conditions. By separately calculating the related mean queue lengths and emergence probabilities, we first derive the expressions of the system loss probability, mean queue length, and mean delay. Finally, we conduct numerous simulations to verify the accuracy of our system model and performance evaluation, which provides the guidance to the buffer design and transmission resource allocation.

Small satellite networks (SSNs) have attracted intensive research interest recently and have been regarded as an emerging architecture to accommodate the ever-increasing space data transmission demand. However, the limited number of on-board transceivers restricts the number of feasible contacts (i.e., an opportunity to transmit data over a communication link), which can be established concurrently by a satellite for data scheduling. Furthermore, limited battery space, storage space, and stochastic data arrivals can further exacerbate the difficulty of the efficient data scheduling design to well match the limited network resources and random data demands, so as to the long-term payoff. Based on the above motivation and specific characteristics of SSNs, in this paper, we extend the traditional dynamic programming algorithms and propose a finite-embedded-infinite two-level dynamic programming framework for optimal data scheduling under a stochastic data arrival SSN environment with joint consideration of contact selection, battery management, and buffer management while taking into account the impact of current decisions on the infinite future. We further formulate this stochastic data scheduling optimization problem as an infinite-horizon discrete Markov decision process (MDP) and propose a joint forward and backward induction algorithm framework to achieve the optimal solution of the infinite MDP. Simulations have been conducted to demonstrate the significant gains of the proposed algorithms in the amount of downloaded data and to evaluate the impact of various network parameters on the algorithm performance.

The emerging distributed satellite cluster network (DSCN) holds great promise in various practical fields, including earth observation, disaster rescue, and tracking of forest fires. In the DSCN environment, it is essential to achieve the best data delivery performance by coordinating multi-dimensional heterogeneous and dynamic resources. However, in real-world applications, the distribution of long-term data arrival is not often fully known. Motivated by this fact, we propose a distributionally robust two-stage stochastic optimization framework with considering the dynamic network resources and the partially known distribution information of long-term data arrival. Aiming at maximizing the total network reward, we formulate a two-stage stochastic flow optimization problem based on the extended time expanded graph. Then, we introduce an ambiguity set for the uncertain distribution of the long-term random data arrival inspired by the idea from the distributionally robust optimization. On the basis of the proposed ambiguity set, we further propose a data arrival distribution robust two-stage recourse (DADR-TR) algorithm by converting the original stochastic optimization problem into a deterministic cone optimization problem, which is computationally tractable. The extensive simulations have been conducted to evaluate the impact of various network parameters on the algorithm performance and further validate that the proposed DADR-TR algorithm can achieve high data delivery performance without full distribution information of the long-term data arrival.

Mission scheduling algorithms are envisioned as critical to satisfy the increasing mission requirements in broadband data relay satellite networks, which is severely influenced by time-varying inter-satellite contacts (i.e., potential available communication links) and differentiated satellite downlink contacts. Nevertheless, the intertwined effect of such two types of contacts on mission schedules poses daunting challenges for the efficient mission scheduling design. In this paper, to achieve fair performance among user satellites, we maximize the minimum number of successfully scheduled missions over all user satellites by jointly optimizing contact plan design, power allocation (PA) in relay satellites, and mission schedules based on the time-expanded graph. The formulated problem is a mixed-integer nonlinear program optimization problem that is challenging to solve. For tractability purpose, we equivalently decompose the problem into a PA problem and an optimal PA-based mission scheduling (OPA_MS) problem, which is still a mixed-integer linear program. We further devise a new two-stage scheme to efficiently solve the OPA_MS problem. Simulation results validate the significant gains of the proposed algorithm in mission completion number and necessitate the consideration of the time-varying and differentiated inter-satellite and downlink contacts.

Small satellite networks (SSNs) are playing an increasing role in nowadays earth observation due to their less development cost and energy consumption. In SSNs, it is pivotal to transmit a huge amount of data for differentiated missions to ground stations. Nevertheless, due to limited transponders and energy budget, not all contacts, i.e., potential available communication links, are feasible in data delivery. Besides, satellite downlink channel conditions are indeed time-varying due to atmospheric precipitation. Therefore, one daunting challenge is searching for feasible contacts termed as contact plan design with consideration of the differentiation for missions. In this paper, we exploit an extended time-evolving graph to characterize network resources. Based on the graph, we formulate the design of mission-aware contact plan, aiming at maximizing network profit in terms of sum weighted data volume as a mixed-integer linear programming. Due to its NP-hardness, we propose a primal decomposition method to efficiently solve the formulated problem by exploiting its special structure. To further reduce the complexity, we propose a link metric considering the issues of residual energy of satellites, time-varying satellite downlink contact capacity, and the differentiation for missions in the conflict graph. Based on the conflict graph, we devise a heuristic algorithm to design contact plan. Simulation results demonstrate the efficiency of the proposed algorithms and necessitate the consideration of the time-varying downlinks and the differentiation of missions for contact plan design.

Small satellite networks, with the advantage of remarkably less development cost and energy consumption with respect to geostationary relay platforms, are playing an increased role in nowadays earth observation. However, small satellites have limited transponders and energy budget, which makes it necessary to design efficient contact plans to improve the network throughput. This paper addresses such an issue of joint management of the energy and transponder resource to well match the mission demand and network resources. We adopt an extended time-evolving graph to characterize network resources and then, formulate the contact plan design problem with the goal of maximizing the throughput as a mixed-integer linear programming. Since the computational complexity of this problem coupling multiple time slots is prohibitive, we further propose two heuristic algorithms which operate on a slot-by-slot basis to achieve high throughput. Simulation results present the impact of different factors on the network performance and moreover, demonstrate that both our contact plan approaches can achieve high throughput with low complexity.

In energy-constrained Low Earth Orbit (LEO) satellite constellations, in order to prolong the network lifetime, more traffic should be carried by the satellites with high battery level, which, in turn, may result in congestion in such satellites. To strike a balance, we study the multi-path routing problem which aims at Maximizing network Lifetime while maintaining a Guaranteed network Congestion level (MLGC). Particularly, we formulate such a problem as a linear programming. However, it is time-consuming that solving the proposed MLGC needs to joint multiple time intervals. Therefore, we further design an Energy Aware Multi-path Routing (EAMR) strategy without solving the optimization problem. Simulation results show that the performance of EAMR is comparable with MLGC and moreover, compared with available routing strategies, the network lifetime can be effectively improved while the required congestion level being guaranteed by implementing our proposed schemes.

In this paper, we investigate the capacity of two- layered satellite networks. Particularly, we propose a unified mathematical framework to formulate the relationship between network capacity and architectural parameters. Then we study the capacity of three typical scenarios. The analytical solutions show that the capacity of individual layer increases linearly with the link bandwidth of that layer. It also increases when there are more orbits and more satellites in each orbit. Moreover, when each LEO satellite can only connect to the nearest MEO satellite, the network capacity is approximately equal to the total capacity of the two layers, and is independent with the architectural parameters such as altitude of both layers and elevation angle of LEO satellites. When each LEO satellite is allowed to connect to all the MEO satellite in its coverage, the network capacity can be further increased. As the coverage size is impacted by the architectural parameters, the network capacity in this case is non-decreasing with the altitude of the MEO layer, and is non-increasing with the altitude of the LEO layer and the elevation angle of the LEO satellites.

A Multilayered Satellite Network (MLSN) tends to be a promising architecture in facilitating global ubiquitous broadband communication. However, unbalanced traffic distribution among its satellite layers should frequently occur, where the lower layers could get relatively congested while the upper layers remain underutilized. This unfair distribution of network traffic can lead to large end-to-end delay and severe throughput degradation. To cope with the above issue, we propose a Tailored Load-Aware Routing (TLAR) strategy to optimally distribute traffic load among the multiple satellite layers, so that the overall traffic congestion in the MLSN is minimized. In TLAR, an optimal portion of network load, which is decided based upon the newly arrived traffic estimation and theoretical analysis of traffic congestion rate in each layer, is detoured through the upper layer. The performance of the proposed routing method has been validated through extensive simulations, which demonstrate that TLAR can significantly alleviate traffic congestion, achieve low end-to-end delay and sustain improved throughput.