Space Information Network

In the co-frequency coexistence problem between the ground 5G system and NGSO (non-geostationary Orbit) constellation system, the interference from the 5G system to NGSO satellites is a typical scenario. Due to the massive number of satellites in the NGSO satellite constellation system, the location, beam direction and beam coverage of satellites are constantly changing. There are issues that the interference calculation amount is large and the actual distribution of the 5G system is difficult to obtain. To address these issues, we analyze the system model and interference principle, put forward the method of 5G system radiation energy to reduce the calculation amount, and present the location analysis method of 5G system based on K-means clustering to reflect the actual distribution of 5G system. Based on this, the interference from the Taiyuan 5G system to the O3b system satellite constellation is simulated. Compared with the existing simulation methods, the proposed method has less computation and is more in line with the actual distribution characteristics of the specific urban 5G system.

Recently, mega-constellation plans, such as Starlink and OneWeb, have been proposed successively. Mega-constellations compose of far more satellites than traditional constellations, with a variety of satellites of different orbital altitudes and complex constellation configurations. A massive number of satellites with different orbital configurations will lead to a significant increase in the computation of the interference probability distribution, and even the failure of common PC to complete the simulation. Based on this, we propose a fast method to calculate the interference probability distribution of mega-constellations, which is especially suitable for calculating satellites with different orbital altitudes and configurations. By dividing the visible airspace of the earth station into sub-airspaces according to probability, the interference from dynamically moving satellites is characterized as the interference caused by static satellites in different sub-airspaces. The interference probability distribution is calculated by combining the occurrence probability of satellites with different orbital altitudes in the sub-airspace. The simulation results indicate that the proposed method greatly improves the computational efficiency and keeps almost the same accuracy as the traditional method. The proposed method can be used to rapidly compute the interference probability distribution of mega-constellations involving multiple orbital altitudes and configurations.

For the demand on differentiated services for aeronautic swarm networks, how to achieve the efficient allocation of network resources to meet the requirements of deep-level cooperative information transmission between air combat platforms becomes an urgent problem to be solved in the development of aeronautic swarm network. Aiming at the case that the underlying physical resources of the aviation swarm network are divided into multiple regions, combined with the characteristics of multi-domain resource mapping, the virtual resource mapping strategy based on multi-domain scenario is studied. Firstly, referring to the existing hybrid hierarchical concept, a new mapping architecture of aeronautic swarm network is designed. Under this architecture, public information is considered based on two aspects of physical resource and network topology to realize information sharing among multiple aviation communication Infrastructure Provider (InP-AC). Then considers dividing the multi-domain mapping into three stages: resource matching, inter-domain mapping and intra-domain mapping. Focuses on the inter-domain mapping phase, considers network cost and inter-domain.

Aiming at the problem of spectrum resource shortage caused by the rapid increase of multimedia data traffic in terrestrial network, a joint scheduling of storage resources and spectrum resources (JSSS) algorithm in an edge computing enabled space-ground integrated information network (SGIN) is proposed to cache and distribute contents, which decouples communication resources from storage resources by means of phased scheduling. In the Multicast Transmission (MT) stage, we match users and base stations (BSs) or satellites based on a heuristic genetic algorithm. In the cache placement (CP) stage, we place contents adaptively based on a multi-agent deep reinforcement learning (MADRL)-based algorithm. Extensive simulations show that compared with traditional methods, the proposed joint scheduling algorithm can effectively improve the cache hit ratio and reduce the bandwidth consumption.

In order to meet the increasing demand for delay-sensitive and computing-intensive applications of maritime users, we first propose an integrated satellite-maritime mobile edge computing framework. The framework considers a maritime mobile communication network consisting of maritime satellites and shipborne base stations, and maritime computation offloading coordinated by the terrestrial cloud and the shipborne edge servers. Secondly, we dynamically calculate the priority that characterizes the urgency of offloading tasks with reinforcement learning. Based on the dynamic priority, a maritime computation offloading method is proposed to optimize the system cost. Finally, simulation results verify the effectiveness and convergence of the proposed method in terms of offloading delay, user energy consumption, offloading response rate and average offloading cost.

With the rapid development of spatial information networks, its applications become more and more popular, and the requirement of the invulnerability of the spatial information network is also improved. Therefore, how to measure the invulnerability of spatial information networks efficiently and accurately has become a hot research topic for current scholars. Firstly, we introduce the research status of invulnerability of spatial information networks. Secondly, considering that the high dynamic characteristics of the spatial information network topology, on the basis of the research on the survivability of complex networks, a new survivability evaluation method for spatial information networks was proposed. The improved jump-range-node method introduces the index of closeness centrality and redefines the normalization factor. The simulation results show that the improved jump-range-node method can effectively distinguish the importance of spatial information network nodes with similar topological structures, and at the same time can reasonably evaluate the invulnerability of spatial information networks.

Due to the wide coverage nature of satellite communication system, Satellite-based Internet of Things (S-IoT) has become a new hot topic in the field of IoT. This paper proposed a differential modulation scheme for LoRa signal to over-come the doppler frequency shift (DFS) in satellite-based IoT from point view of modulation technique. Furthermore, a maximum likelihood sequence detection (MLSD) demodulation algorithm is also designed at the satellite receiver to optimize error spread phenomenon of proposed differential modulation scheme. Simulation results show that the proposed differential modulation LoRa signal has better bit-error-ratio (BER) performance compared with traditional LoRa modulation signal in LEO S-IoT.

With the development of space missions and the diversity of satellites, space information networks have become more complex. Space TT&C (Tracking Telemetry and Command) missions are faced with shortage of resources and complex demand characteristics. In view of these challenges, we propose a hierarchical progressive TT&C mission planning algorithm. Firstly, we model the TT&C mission planning problem as a mixed integer linear programming problem with aiming at maximizing the network reward, i.e., the weighted number of completed TT&C missions. Then, we decompose this problem into multiple levels of optimization sub-problems according to the types of constraints and use time buckets and binary conflict trees to reduce the complexity of the algorithm. Finally, we use the binary conflict tree to perform local disturbances Simulation results show that, compared with the existing planning algorithms, the proposed algorithm not only guarantees the efficiency of the algorithm, but also improves the total reward of TT&C mission planning.

Unmanned aerial vehicle (UAV) base station has been proposed as a promising solution in emergency communication and supplementary communication for terrestrial networks due to its flexible layout and good mobility support. However, the dense deployment of UAV base station and ground base station brings great challenges in the configuration of neighbor cell list (NCL) during handover process. This paper presents a Cascading Bandits based Mobility Management (CBMM) algorithm for NCL configuration in the low altitude heterogeneous networks, where online learning is used to exploiting the historical handover information. In addition to the received signal strength, the cell load of each base station is also considered in the handover procedure. We aim at optimizing the configuration of NCL, so as to improve handover performance by increasing the probability of selecting the best target base station while at the same time reducing the selection delay. It is proved that the signaling overhead can be effectively reduced, since the proposed CBMM algorithm can significantly cut down the number of candidate base stations in NCL. Moreover, by ranking the candidate base stations according to their historical performance, the number of measured base stations in handover preparation phase can be effectively reduced to avoid extra delay. The simulation results of the proposed algorithm and other two existing solutions are presented to illustrate that the CBMM algorithm can achieve efficient handover management.

IP multicast has the advantages of high network utilization, low bandwidth overhead and strong scalability, which plays a great role in a large number of terrestrial Internet applications. However, in satellite communication network, there are more applications for unicast environment, but less for multicast environment. The main reason is that the limited link bandwidth and high channel error rate restrict the application of multicast technology to a certain extent. Under the condition of satellite network bandwidth and multicast channel increasing, the performance of satellite communication channel is greatly improved. This paper analyzes the mode and protocol of network data transmission, and designs a method of distributing data files by using multicast.

In the multi-target TT&C system of relay satellite, due to the different distances between users and satellites, the strong signals of near users will restrain the weak signals of long-distance users and reduce the system capacity. This paper studies the power control algorithm in the reverse link of multi-target TT&C system. Specifically, the signal model of multi-target TT&C system is established, and two power adjustment algorithms, namely fixed step adjustment algorithm and adaptive step adjustment algorithm, are analyzed. The adaptive step adjustment algorithm is improved, and the upper limit of step adjustment is introduced. The simulation results show that the algorithm is effective in multi-target TT&C system.

Graph computing is more and more widely used in various fields such as spatial information network and social network. However, the existing graph computing systems have some problems like complex programming and steep learning curve. This paper introduces GRAPE, a distributed large-scale GRAPh Engine, which has the unique features of solid theoretical guarantee, ease of use, auto-parallelization and high performance. The paper also introduces several typical scenarios of graph computing, including entity resolution, link prediction, community detection and graph mining of spatial information network. In these scenarios, various problems have been encountered in the existing systems, such as failure to compute over large-scale data due to the high computation complexity, loss of accuracy due to the cropping of original data and too long execution time. In the face of these challenges, GRAPE is easy to support these computing scenarios with a series of technical improvements. With the deployment of GRAPE in Alibaba, both effectiveness and efficiency of graph computing have been greatly improved.

In recent years, software defined networking (SDN) has been a promising tool for achieving flexible satellite networks, exploiting the advantage that it follows the idea of “renewing switch functions without changing hardware”. Southbound interfaces (SBIs) are recognized as pivotal technologies in SDN for flexible network management and control, though, are faced with tough challenges posed by satellite networks. The OpenFlow, a representative and pervasive protocol in existing SBIs, only applies to wired communication systems such as Ethernet. However, satellite networks have unique characteristics in structure and service types distinguished from terrestrial wired networks, which means the unmodified implementation of existing SBIs cannot satisfy the needs of satellite networks. Based on the problem above, this paper aims to redesign a SBI well fitting to heterogeneous software defined satellite networks. Firstly, this paper analyzes the applicability of existing SBIs in a heterogeneous scenario comprising remote sensing satellites and relay satellites. Secondly, a SBI design framework is proposed, in which a scheme of building an intermediate layer on top of the OpenFlow is presented for reducing the protocol complexity. This framework gives a paradigm of the OpenFlow extension and serves as a exploratory work for achieving unified resource management in software-defined satellite networks.