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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. [ABSTRACT FROM AUTHOR]

Mobile relaying is envisioned as a promising technology to alleviate the masking effect in satellite links. Nevertheless, due to the mobility and limited communication range, the network topology in mobile relay networks becomes highly dynamic and time-varying, resulting in increased difficulty of network optimization. This letter investigates unmanned aerial vehicles (UAVs) aided hybrid satellite-terrestrial network, where UAVs are served as mobile relay base stations to assist the communication between the satellite and ground users. We aim to maximize the number of served users by optimizing UAV trajectory and user link selection. To solve the formulated mixed-integer and non-convex optimization problem, we first find the optimal link selection via a designed graph neural network (GNN), and then adjust the UAV locations by using model-free reinforcement learning (RL), alternately. Numerical results demonstrate that our proposed scheme is superior to the state-of-the-art RL algorithms. [ABSTRACT FROM AUTHOR]

Network structure has significant impact on its data transmission and robustness. This article presents a framework to evaluate the structure performance of dynamic networks, with a focus on measuring the topological dynamics and assessing its effect on routing, transmission efficiency, network robustness, and network capacity. Based on this framework, the structural performance of satellite networks including both single-layered and multilayered satellite networks is evaluated systematically. The performance results reveal that it is difficult to design the efficient and feasible transmission protocols for multilayered satellite networks for their high-dynamic topologies with irregular changes, which brings unpractical requirements on the routing update. To cope with this problem, a synchronization mechanism of the interorbital links for multilayered satellite networks is presented, which can significantly reduce and regularize the topological dynamics of multilayered satellite networks. Its advantages can effectively facilitate the realization of transmission protocols for multilayered satellite networks. Finally, the experimental evaluation results demonstrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

The utilization of extremely high-frequency (EHF) bands can achieve very high throughput in satellite networks (SatNets). Nevertheless, the severe rain attenuation at EHF bands imposes strict limitations on the system availability. Smart gateway diversity (SGD) is considered indispensable in order to guarantee the required availability with reasonable cost. In this context, we examine a load-sharing SGD (LS-SGD) architecture, which has been recently proposed in the literature. For this diversity scheme, we define the system outage probability (SOP) using a rigorous probabilistic analysis based on the Poisson binomial distribution, and taking into consideration the traffic demand as well as the gateway (GW) capacity. Furthermore, we provide several methods for the exact and approximate calculation of SOP. As concerns the exact computation of SOP, a closed-form expression and an efficient algorithm based on a recursive formula are given, both with quadratic worst-case complexity in the number of GWs. Finally, the proposed approximation methods include well-known probability distributions (binomial, Poisson, normal) and a Chernoff bound. According to the numerical results, binomial and Poisson distributions are by far the most accurate approximation methods. [ABSTRACT FROM AUTHOR]

Due to the capability of simultaneously delivering the same information to multiple users, the multicast transmission has been widely applied over the satellite networks. Furthermore, more and more information needs to ensure the transmission delay within a predefined bound. However, the satellite networks possess the time-varying characteristics and limited energy, which causes challenges for the low delay multicast design. In this paper, we investigate the energy-efficient delay-bounded (EEDB) multicast problem to accomplish the multicast transmission within the given delay bound, but to consume energy as less as possible. Specially, with the help of the time-expanded graph (TEG), we construct the multicast time-expanded graph (MTEG) through introducing some auxiliary vertices and arcs to accurately depict the across-time multicast transmission and the “wireless multicast advantage”. Then, we formulate the EEDB multicast problem as one combinatorial optimization and propose an iterative heuristic algorithm to achieve the suboptimal multicast scheme. Simulation results are also presented to verify our proposed algorithm. [ABSTRACT FROM AUTHOR]

Allowing multiple users access simultaneously, a power-domain nonorthogonal multiple access (NOMA) scheme can provide enhanced resource utilization efficiency and has been viewed as a promising strategy in the satellite networks, to provide high quality-of-service for large number of users within limited spectrum and time resources. Taking into account the random location information of served users, imperfect channel state information (CSI), and antenna-pointing error, we study the ergodic capacity of a NOMA-based uplink satellite network in this article. Specially, we first develop a general framework for the NOMA-based system by applying standard recommendations, configurations, and channel characteristics. Then, the analytical expression for the achievable ergodic capacity of the considered network is derived. Finally, numerical simulations are provided to attest the validity of theoretical results and show the impact of various key parameters, such as location information, link quality, transmission power, imperfect CSI, and antenna-pointing error, on the considered network. [ABSTRACT FROM AUTHOR]

In this paper, random pattern multiplexing (RPM) for random access (RA) is proposed to improve the throughput in Internet of Things-oriented satellite networks. The pattern is defined as the mapping scheme of packet onto a resource block (RB) that consists of multiple resource elements (REs). The patterns are randomly selected by active satellite machine-type-communication terminals (SMTs) from a set of available patterns, and are employed to differentiate signals of different SMTs sharing a RB. Message passing detection algorithm is employed to decode multiple superimposed packets, which enables multiple packet reception capability in the RA scheme. The proposed RPM can be combined with contention resolution diversity slotted ALOHA (CRDSA), which generates RPM–CRDSA protocol. In this protocol, the interference cancellation technique can be carried out across RBs to remove the received signals that are already decoded, which further improves the throughput. The performance of RPM–CRDSA is evaluated via mathematical analysis and computer simulation. The probability of preamble collision and an upper bound of throughput are derived, respectively. Simulation results show that the peak normalized throughput of RPM–CRDSA with $N = 4$ REs can reach 2.03 b/symbol, which largely outperforms traditional protocols. [ABSTRACT FROM AUTHOR]

In this paper, we propose a spatial group based optimal uplink power control scheme to enhance the performance of random access (RA) in satellite networks. In the proposed scheme, satellite machine-type terminals (SMTs) are divided into multiple groups based on their spatial locations in a distributed fashion, and the target received power levels of SMTs are the same in the same group but different from group to group by means of uplink power control, which enables the received power diversity. The received power levels of SMTs in different groups are designed by taking into account their path losses, which effectively reduces the energy consumption of SMTs. The threshold based capture model at the gateway demodulator is adopted, and the packet loss ratio (PLR) based on this model is derived by using density evolution analysis. An optimization problem is formulated and solved to jointly find the optimal number of power levels and the optimal values of power levels, which maximizes the RA system throughput. Simulation results show that the proposed scheme can obtain considerable performance improvement with respect to the conventional schemes in terms of normalized throughput, PLR, and average energy consumption. [ABSTRACT FROM AUTHOR]

The development of satellite networks is drawing much more attention in recent years due to the wide coverage ability. Composed of geosynchronous orbit (GEO), medium earth orbit (MEO), and low earth orbit (LEO) satellites, the satellite network is a three-layer heterogeneous network of high complexity, for which comprehensive theoretical analysis is still missing. In this paper, we investigate the problem of capacity management in the three-layer heterogeneous satellite network. We first construct the model of the network and propose a low-complexity method for calculating the capacity between satellites. Based on the time structure of the time expanded graph, the searching space is greatly reduced compared to traditional augmenting path searching strategies, which can significantly reduce the computing complexity. Then, based on Q-learning, we proposed a long-term optimal capacity allocation algorithm to optimize the long-term utility of the system. In order to reduce the storage and computing complexity, a learning framework with low-complexity is constructed while taking the properties of satellite systems into account. Finally, we analyze the capacity performance of the three-layer heterogeneous satellite network and also evaluate the proposed algorithms with numerical results. [ABSTRACT FROM AUTHOR]

As the typical delay tolerant networks, the satellite networks possess the intermittent connections, the large-scale time delays and the time-varying topologies. Obviously, such features seriously affect the delivery of mission data with certain requirements on the traffic or latency. To decrease the delivery time, existing relay-based contact graph routing (CGR) method selects the earliest reachable path to forward mission data. However, this method cannot guarantee the transmission of a large amount data and wastes the rare contact opportunities. Therefore, in this paper, we focus on the transmission QoS problem of multiple missions over the satellite networks, and design a QoS support routing strategy to achieve multiple flow-maximizing paths with acceptable delivery delays. Especially, with the storage time aggregated graph (STAG), we construct an on-demand mission model to depict both the network dynamic characteristics and the different mission requirements, and then reduce the QoS support problem as a graph-based maximum flow problem. To solve this problem, one STAG-based multiple flow-maximizing routing scheme is proposed to ensure the mission QoS and match the rare network resources, which has low computation complexity. Finally, compared with CGR, simulation results demonstrate the proposed scheme can achieve a higher mission completion rate and resource utilization rate. [ABSTRACT FROM AUTHOR]

The multi-commodity flow problem plays an important role in network optimization, routing, and service scheduling. With the network partitioning and the intermittent connectivity, the commodity flows in delay tolerant networks (DTNs) are time-dependent, which is very different from that over the static networks. As an NP-hard problem, existing works can only obtain sub-optimal results on maximizing the multi-commodity flow of dynamic networks. To overcome these bottlenecks, in this paper, we propose a graph-based algorithm to solve the maximum two-commodity flow problem over the DTNs. Through analyzing the relationship between the two commodities, we propose a maximum two-commodity flow theorem to simplify the coupling two-commodity flow problem as the two single-commodity flow ones. Then, with the help of the storage time aggregated graph (STAG) (a DTN model with less memory), we construct a pair of flow graphs to describe the reduced two single-commodity flows (addition flow and subtraction flow), and design the corresponding flow calculation methods. Moreover, we design a STAG-based dynamic combined flow algorithm to maximize the two-commodity flow. Finally, the computational complexity of the proposed algorithm is analyzed, and its efficacy has also been demonstrated through an illustrative example and numerical simulations. [ABSTRACT FROM AUTHOR]

Structural centrality metrics of the networks are important features to identify central nodes or links, to assess network vulnerability against removal of nodes or links, and to manage or control the network traffic to reduce the network congestion. Classic graph-based centrality metrics are not effective in dynamic space satellite networks due to the changing connectivity and they cannot effectively capture important temporal properties. In this paper, we employ a new technique called time-varying graphs (TVGs) to express temporal concepts and definitions of space satellite networks and propose several temporal centrality metrics in order to account for the temporal features of the networks. To calculate these temporal centrality metrics, we adopt a modified matrix multiplication algorithm to find the shortest journeys between nodes in the TVG. A temporal centrality-balanced traffic management scheme is further developed to enhance the network performance, which is integrated into a temporal centrality-balanced routing algorithm. To show the performance of the proposed centrality metrics and centrality balanced routing algorithm, we conduct some experiments performed on a scenario of the LEO satellite network. The simulation results show that the proposed centrality metrics are more powerful to analyze the evolution of time-varying centrality and vulnerability of space satellite networks than topological adaptation of vanilla centrality methods. Besides, the proposed centrality-balanced routing algorithm reaches the superior quality values of the network performance (in terms of several factors) in comparison with those attained by the traditional shortest journeys routing algorithm and proves to be more powerful to support the high traffic load. [ABSTRACT FROM PUBLISHER]

As there is a growing demand for providing high-definition (HD) video and satellite TV services over satellite networks, interconnection and multimedia applications need new-type satellite services to ensure good quality-of-experience (QoE) for these Internet flows. The characteristics of satellite channels (notably the lossy and long-delay environment) can heavily influence transferring protocols exclusively designed for terrestrial network, and using a radically reliable method (TCP) or unreliable method (UDP) is not the best way for the transmission of multimedia applications over satellite networks, such as HD video. In order to cope with the contradiction between high quality and real-time transfer, this paper presents a retransmission-based partially reliable transfer protocol, called automatically partially reliable transfer protocol (APRT). For the purpose of tracing the network status, the hidden Markov model (HMM) is employed to depict reliable level of the transfer strategy. Through off-line training initialization and online prediction, current network status is then mapped to a reliable level, which can represent good video quality and minimum packet delay. Meanwhile, APRT can retransmit the lost packets as the network changing without losing key frames. Extensive simulation results show that for both single and coexisting flow scenarios, APRT can improve video quality of services (QoSs) against reliable and unreliable transfer strategies in terms of different metrics. For single video flow, PSNR of APRT outperforms than that of other strategies under lossy and long-delay networks up to 36.53% and achieves short packet delay. For coexisting scenario, APRT shows good performance in terms of latency and video quality. [ABSTRACT FROM AUTHOR]

Space information network (SIN) is a promising networking architecture to significantly broaden the observation area and realize continuous information acquisition for earth observation. Over the dynamic and complex SIN environment, it is a key issue to coordinate multi-dimensional heterogeneous network resources (e.g., observation resource and transmission resource) in the presence of multi-resource variations and severe conflicts, such that diverse earth observation service requirements can be satisfied. To this end, this paper studies the multi-resource coordinate scheduling problem in SINs. Specifically, we first characterize the relationship among multi-resource using an event-driven time-expanded graph (EDTEG). Based on the EDTEG, observation resource and transmission resource are jointly considered, and an integer linear programming optimization problem is formulated to maximize the sum priorities of the successfully scheduled tasks. An iterative optimization technique is employed to decompose the problem into separate observation scheduling and transmission scheduling sub-problems, which can be efficiently solved by extended transmission time sharing graph and directed acyclic graph methods, respectively. Simulation results demonstrate the effectiveness of the proposed algorithm and performance impacts of different network parameters. [ABSTRACT FROM PUBLISHER]

To extend the network coverage to rural and remote areas, there are different solutions with their advantages and disadvantages. We propose to use a nanosatellite network and to exploit the ability to cope with large delays and disruptions provided by the delay- and disruption-tolerant networking (DTN) paradigm. The connection from remote areas is managed through ground stations called cold spots that collect data from rural nodes and address them to the nanosatellites. Nanosatellites carry and download data to hot spots that address them to the Internet destinations. On the reverse direction, Internet data are addressed to hot spots, uploaded on nanosatellites and delivered to the rural destinations through cold spots. The problem of choosing the “optimal’’ hot spot to address data from the Internet source is important because a wrong choice could lead to large delivery delays. In this paper, we propose “gRANteD’’: a Nanosatellite-DTN Network for rural and remote areas. gRANteD includes “HotSel,’’ a hot spot selection algorithm to minimize the delivery time of all data destined to rural users. The performance evaluation is carried out through a DTN module which implements gRANteD and HotSel, within the framework of Network Simulator 3. [ABSTRACT FROM PUBLISHER]

Contact plan design (CPD) aims to schedule conflicting forthcoming communication opportunities (i.e., contacts). In CPD, the capacity of established contacts is indeed time-varying. Neglecting such variations in contact capacity inevitably leads to system performance deterioration. In this letter, we develop a new dynamic CPD algorithm while considering the time-varying property of contacts in broadband data relay satellite networks. The novelty lies in that we treat the flow optimization problem in a time-expanded graph as a queue-stability-related stochastic optimization problem, such that the problem can be solved independently slot by slot. Furthermore, we prove that the proposed online algorithm can approach arbitrarily close to the optimal throughput. Simulation results validate the efficiency of our proposed algorithm. [ABSTRACT FROM PUBLISHER]

Feedback implosion is a major problem limiting scalability in multicast satellite networks that arises when a large number of users transmit their feedback messages (FBMs) through the uplink channel, occupying a significant portion of system resources. This paper investigates how suppression of FBMs may be achieved through a novel feedback suppression scheme in which each user unilaterally decides whether to send a FBM or not. The users decisions are modeled applying game theory with inaccurate information. For this problem, the Bayesian equilibrium for the two and N-player game is investigated. It is proved that the feedback suppression game has a unique Bayesian equilibrium point. Then, based on appropriate numerical and simulation results, it is demonstrated that the proposed scheme avoids feedback implosion while at the same time it keeps feedback latency at very low levels. Finally, it is shown that the proposed scheme exhibits a significant performance improvement compared to existing exponential timer-based schemes. [ABSTRACT FROM PUBLISHER]

In this letter, a reservation scheme with contention resolution diversity slotted ALOHA (CRDSA), called R-CRDSA, is proposed. Although CRDSA promises excellent performance compared to slotted ALOHA (SA), there are two barriers to be overcome for its use in satellite networks. The first is a low critical point of an offered load; i.e., at this point, as the offered load increases, the throughput will decrease. CRDSA has a lower critical point than that of SA. The second barrier is that CRDSA has a lower capacity efficiency compared to SA; i.e., on average, CRDSA requires more power or bandwidth than SA for transmission. R-CRDSA can overcome these barriers at the same cost as CRDSA, especially for multi-packet messages. The analytic performances are derived by the observation of the transition between two successive frames, and the average number of packets in a message. The simulation results show that the analytic derivations are valid, and they confirm that the barriers are resolved by R-CRDSA. [ABSTRACT FROM PUBLISHER]

The transmission of confidential information over satellite links operating at frequencies above 10 GHz is studied in this paper. The major factor impairing the link performance at these frequencies is rain attenuation, a physical phenomenon exhibiting both spatial and temporal variation. Based on an accurate channel modeling, analytical expressions of the probability of nonzero secrecy capacity and the outage probability for this type of networks are provided, giving an information-theoretic approach of the problem of secure transmission for satellite networks. The analysis is extended in the case of two legitimate users and two eavesdroppers where the diversity gain increases or decreases, respectively, the probability of secure transmissions. Useful conclusions are drawn concerning the impact of various factors, such as frequency of operation, separation angles and climatic conditions on the aforementioned metrics through extended numerical results. [ABSTRACT FROM PUBLISHER]