Space-air-ground integrated network (SAGIN), as an integration of satellite systems, aerial networks, and terrestrial communications, has been becoming an emerging architecture and attracted intensive research interest during the past years. Besides bringing significant benefits for various practical services and applications, SAGIN is also facing many unprecedented challenges due to its specific characteristics, such as heterogeneity, self-organization, and time-variability. Compared to traditional ground or satellite networks, SAGIN is affected by the limited and unbalanced network resources in all three network segments, so that it is difficult to obtain the best performances for traffic delivery. Therefore, the system integration, protocol optimization, resource management, and allocation in SAGIN is of great significance. To the best of our knowledge, we are the first to present the state-of-the-art of the SAGIN since existing survey papers focused on either only one single network segment in space or air, or the integration of space-ground, neglecting the integration of all the three network segments. In light of this, we present in this paper a comprehensive review of recent research works concerning SAGIN from network design and resource allocation to performance analysis and optimization. After discussing several existing network architectures, we also point out some technology challenges and future directions.

Space-Air-Ground Integrated Network: A Survey

In remote regions (e.g., mountain and desert), cellular networks are usually sparsely deployed or unavailable. With the appearance of new applications (e.g., industrial automation and environment monitoring) in remote regions, resource-constrained terminals become unable to meet the latency requirements. Meanwhile, offloading tasks to urban terrestrial cloud (TC) via satellite link will lead to high delay. To tackle above issues, Satellite Edge Computing architecture is proposed, i.e., users can offload computing tasks to visible satellites for executing. However, existing works are usually limited to offload tasks in pure satellite networks, and make offloading decisions based on the predefined models of users. Besides, the runtime consumption of existing algorithms is rather high.In this paper, we study the task offloading problem in satellite-terrestrial edge computing networks, where tasks can be executed by satellite or urban TC. The proposed Deep Reinforcement learning-based Task Offloading (DRTO) algorithm can accelerate learning process by adjusting the number of candidate locations. In addition, offloading location and bandwidth allocation only depend on the current channel states. Simulation results show that DRTO achieves near-optimal offloading cost performance with much less runtime consumption, which is more suitable for satellite-terrestrial network with fast fading channel.

Deep Reinforcement Learning-based Task Offloading in Satellite-Terrestrial Edge Computing Networks

Low Earth Orbit (LEO) satellites play a central role in Earth observation systems. Due to high-speed movement, LEO satellites have very limited contact time with earth stations (ESs). Satellites that store too much data need to wait until they reconnect to ESs. In this case, the system may not meet the delay requirement. Existing works have scheduled the data downloading from satellites to ESs and made use of inter-satellite links (ISLs) to offload data among other satellites. However, such algorithms only maximize the overall throughput but neglect the truth that energy is always a scarce resource for satellites. To solve this problem, based on the proposed enhanced superposed multi-power level multi-transmission (ESMLMT) graph, we propose an iterative algorithm to minimize the energy consumption of data transmission while maximizing the throughput and satisfying the delay requirement. It is shown by simulations that our proposed algorithm achieved significantly higher energy efficiency without data downloading throughput performance degradation in satellite systems.

Energy-Efficient Collaborative Data Downloading by Using Inter-Satellite Offloading

Broadcast is a fundamental operation in satellite networks. It is frequently used for satellites’ self-organization, routing discovery, coordination, and collaboration. Meanwhile, many satellite network applications require that a broadcast message from a source node must be received by all the other nodes within a given delay bound. In this paper, we study the delay bounded and minimal energy broadcast problem in satellite networks. The broadcast routing problem is challenging in satellite networks due to highly dynamic change and intermittent connectivity of the network. To solve this problem, we propose an algorithm that is based on the model of multi-power level multi-transmission space-time graph. The algorithm repeatedly finds the most energy efficient “caterpillars” in the graph, and merges these caterpillars into an energy efficient broadcast tree. Simulations have been conducted in real satellite constellations and the results show that our proposed scheme can achieve significant energy saving for broadcast in satellite networks, compared with other existing methods.

Minimal Energy Broadcast for Delay-Bounded Applications in Satellite Networks

Near real time monitoring system utilizing earth observation satellite constellation is expected for disaster management. However, these satellites may not have sufficient time to transmit all of their observed data to a ground station because low earth orbit (LEO) satellites pass over a ground station for a few minutes. Therefore, if the satellite stores more data than the size of transmission data to a ground station, it needs to transmit residual data after approaching other ground stations. As a result, it causes lack of timeliness due to the overhead time for fine tracking between a satellite and a new target of ground station. To deal with above mention, this paper proposes novel routing method to improve transfer ratio of whole observed data in a contact time. Relative to the conventional method which transmits data to a ground station, the proposed method divides observed data and modulates them as multi-carrier signals so as to distribute to two or more ground stations by multi-hop relaying. Furthermore, the satellite of this system switches inter-satellite link and satellite-to-ground link for own data and other satellite''''s data utilizing channelizer dynamically. The effectiveness of our proposal is verified through simulation results.

Multi-Carrier Relaying for Successive Data Transfer in Earth Observation Satellite Constellations

In recent years a large number of LEO (Low Earth Orbit) satellites have been launched into space for various applications, such as weather forecast, environment monitoring and military surveillance. One important task of these satellites is to collect data and download them to Earth Stations (ESs) for further processing. Since LEO satellites fly at high speeds and have very limited contact time with ESs, they may not have sufficient time to download data to the ground. Existing work focused on scheduling data downloading from satellites to the ES such that the throughput of the downloading is maximized. However, what the scheduling algorithms can do is very limited in this scenario due to the disparity of the contact time a satellite has and the amount of data it carries for downloading. In this paper, we propose a novel scheme that uses inter-satellite links (ISLs) to offload data among satellites before they contact with the ES. A satellite with large amount of data but little contact time can offload data to other satellites that have surplus contact time. By offloading data among satellites on-the-fly in space, each satellite can carry the right amount of data when it comes to download its data to the ES, such that its contact time can be fully utilized. Extensive simulations have been conducted to evaluate the effectiveness of our proposed method. The simulation results show that the data downloading throughput by using ISL data offloading can be increased significantly.

Optimal Data Downloading by Using Inter-Satellite Offloading in LEO Satellite Networks

There has been explosive growth of satellite applications in the past decades. Broadcast is a fundamental operation in satellite networks, which is required for satellites self-organization, routing discovery, coordination and collaborations. In this paper, we study the problem of delay bounded and energy efficient broadcast in satellite networks. Broadcast routing is a challenging issue in satellite networks due to highly dynamic change and intermittent connectivity of the network. We propose a method that is based on a Multi-power Level Multi-Transmission (MLMT) Space-Time Graph. The algorithm repeatedly finds the most energy efficient ‘caterpillars’ in the graph, and merges caterpillars into an energy efficient broadcast tree. Simulations have been conducted in the real satellite constellations and the results show our proposed scheme has achieved good performance compared with other broadcast algorithms.

Wenqiang Liu

Papers

Optimal Data Downloading by Using Inter-Satellite Offloading in LEO Satellite Networks

Delay-bounded and minimal energy broadcast in satellite networks with multi-power levels