Remote sensing satellite (RSS) plays an increasingly important role in satellite networks. Current studies have paid wide attention to system modeling and performance analysis of RSS traffic acquisition, storing and transmission processes. However, in traffic acquisition process, the transitions between the "on" state and the "off" state of the on-board sensor generally exhibit a Markovian feature. Besides, the continuous stream traffic arrives in the "on" states and no traffic arrives in the "off" states. These features have not been sufficiently investigated. Meanwhile, the idiomatic Poisson traffic models are no more accurate, which inevitably brings great challenges to precise performance analysis. Aiming at above features, we present a Markov Modulated Deterministic Process (MMDP) model to simulate the traffic acquisition process. Afterwards, according to the global coverage of relay satellites, we describe the integrated traffic acquisition, storing and transmission processes as an MMDP/D/1/K queueing model. Further, we derive the closed-form expressions of some important quality of service indices (i.e., the loss probability, the average queue length and the average delay). Finally, we conduct numerous simulations to verify the effectiveness of theoretical results.

By integrating software defined network and network function virtualization, software defined satellite networks (SDSNs) can enable flexible virtual network function (VNF) deployment to process and forward end-to-end traffic flows. Since one traffic flow has to go through all its required VNFs, the VNF deployment has a significant impact on traffic routing. In this regard, with time-varying network topology and limited network resources taken into account, we aim to match VNF deployment and routing to fulfill traffic flows' requirements in the SDSN in a cost-effective manner. Specifically, we first evolve the traditional time evolving graph as a software defined time evolving graph (SDTEG) to depict the time-varying network topology and meanwhile, provide a shared platform for elastic network resource provisioning. On this basis, we then formulate a cost minimization problem as a multi-slot integer linear programming problem to make a judicious decision on VNF deployment and routing for each traffic flow. To address this challenging problem effectively, we further propose a heuristic algorithm, referred to as time-slot decoupled algorithm (TDA). Finally, the effectiveness of the TDA as well as the superiorities from the joint optimization of VNF deployment and routing are demonstrated through simulation results.

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.

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.

In Earth observation satellite networks (EOSNs), imbalance between the observation and transmission opportunities can cause poor network performance, e.g., a reduced number of successfully scheduled targets. To tackle this issue, in this paper, we investigate the multi-dimensional resource scheduling problem in EOSNs to ensure that each EOS can observe an appropriate subset of targets with matched downloading capacity to the destination. Specifically, an optimization problem is formulated and proved to be NP-hard. Then, an iterative optimization technique is employed to decompose the problem into separate observation scheduling and transmission scheduling subproblems, which are further efficiently solved by acyclic directed graph and particle optimization methods, respectively. Extensive simulations have been conducted to demonstrate the efficiency of the proposed scheduling algorithm.

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.

An important consideration for LEO satellite networks is choosing suitable routes to prolong the network lifetime while stringently guarantee the throughput requirement. However, both the highly dynamic network topology and intrinsically time-varying renewable energy availability pose great constraints and challenges in designing such routing schemes. To solve the problem, we resort to Capacity Region Evolving Graph (CREG) and formulate the throughput constrained maximum lifetime routing problem. Unfortunately, solving the problem without exploiting its special structure is indeed time-consuming, since multiple time intervals must be jointly handled. Two efficient routing algorithms, namely, Maximum Lifetime Routing (MLR) and Shortest Path-based Progressive Routing (SPPR), are thus proposed to reduce the execution time of solving the routing problem. Specifically, MLR decomposes the problem into multiple independent subproblems without trading its optimality, while SPPR exploits the deterministic mobility of satellite networks without solving the optimization problem. Simulation results verify that prolonged network lifetime and balanced traffic distribution can be obtained for both the routing algorithms.

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.

Successive Interference Cancellation (SIC) is a potentially powerful technique for improving the performance of multi-hop wireless networks, owing to its ability to enable concurrent receptions from multiple transmitters as well as interference rejection. In this paper, we address the problem of finding the route with maximal end-to-end spectral efficiency in multi-hop wireless networks, under the constraint of optimal bandwidth sharing. By taking advantage of SIC, more transmission opportunities are exploited by the nodes along the selected path. We formulate a cross-layer optimization framework to quantify the spectral efficiency improvement with SIC and then make use of several structural properties to derive exact solutions. Additionally, three SIC-based routing alternatives with low computational complexity are proposed, on the basis of the conventional shortest path algorithm, to obtain spectrum-efficient routes. Numerous simulation results verify that SIC can bring significant gains in terms of spectral efficiency.