Space-Air-Ground Integrated Network with Native Intelligence (NI-SAGIN): Concept, Architecture, Access and Networking, No. 1, 2022
Editor: Zhenyu Xiao
Publication year
Article type
Sort by Default Latest Most read  
Please wait a minute...
  • Select all
  • Guest Editorial
    Fei Huang, Guangxia Li, Shiwei Tian, Jin Chen, Guangteng Fan, Jinghui Chang
    China Communications. 2022, 19(1): 202-217.
    Unmanned aerial vehicles (UAVs) are increasingly considered in safe autonomous navigation systems to explore unknown environments where UAVs are equipped with multiple sensors to perceive the surroundings. However, how to achieve UAV-enabled data dissemination and also ensure safe navigation synchronously is a new challenge. In this paper, our goal is minimizing the whole weighted sum of the UAV's task completion time while satisfying the data transmission task requirement and the UAV's feasible flight region constraints. However, it is unable to be solved via standard optimization methods mainly on account of lacking a tractable and accurate system model in practice. To overcome this tough issue, we propose a new solution approach by utilizing the most advanced dueling double deep Q network (dueling DDQN) with multi-step learning. Specifically, to improve the algorithm, the extra labels are added to the primitive states. Simulation results indicate the validity and performance superiority of the proposed algorithm under different data thresholds compared with two other benchmarks.
  • Guest Editorial
    Qihui Wu, Min Zhang, Chao Dong, Yong Feng, Yanli Yuan, Simeng Feng, Tony Q. S. Quek
    China Communications. 2022, 19(1): 186-201.
    In recent years, with the growth in Unmanned Aerial Vehicles (UAVs), UAV-based systems have become popular in both military and civil applications.In these scenarios, the lack of reliable communication infrastructure has motivated UAVs to establish a network as flying nodes, also known as Flying Ad Hoc Networks (FANETs).However, in FANETs, the high mobility degree of flying and terrestrial users may be responsible for constant changes in the network topology, making end-to-end connections in FANETs challenging.Mobility estimation and prediction of UAVs can address the challenge mentioned above since it can provide better routing planning and improve overall FANET performance in terms of continuous service availability.We thus develop a Software Defined Network (SDN)-based heterogeneous architecture for reliable communication in FANETs.In this architecture, we apply an Extended Kalman Filter (EKF) for accurate mobility estimation and prediction of UAVs.In particular, we formulate the routing problem in SDN-based Heterogeneous FANETs as a graph decision problem.As the problem is NP-hard, we further propose a Directional Particle Swarming Optimization (DPSO) approach to solve it.The extensive simulation results demonstrate that the proposed DPSO routing can exhibit superior performance in improving the goodput, packet delivery ratio, and delay.
  • Guest Editorial
    Bowen Zeng, Zhongshan Zhang, Xuhui Ding, Xiangyuan Bu, Jianping An
    China Communications. 2022, 19(1): 166-185.
    The cooperation of multiple Unmanned Aerial Vehicles (UAVs) has become a promising scenario in Space-Air-Ground Integrated Networks (SAGINs) recently due to their widespread applications, where wireless communication is a basic necessity and is normally categorized into control and non-payload communication (CNPC) as well as payload communication.In this paper, we attempt to tackle two challenges of UAV communication respectively on establishing reliable CNPC links against the high mobility of UAVs as well as changeable communication conditions, and on offering dynamic resource optimization for Quality-of-Service (QoS) guaranteed payload communication with variable link connectivity. Firstly, we propose the concept of air controlling center (ACC), a virtual application equipped on the infrastructure in SAGINs, which can collect global information for estimating UAV trajectory and communication channels. We then introduce the knapsack problem for modelling resource optimization of UAV communication in order to provide optimal access points for both CNPC and payload communication.Meanwhile, using the air controlling information, predictive decision algorithm and handover strategy are introduced for the reliable connection with multiple access points.Simulation results demonstrate that our proposal ensures an approximate always-on reliable accessing of communication links and outperforms the existing methods against high mobility, sparse distribution, and physical obstacles.
  • Guest Editorial
    Yanpeng Dai, Bin Lin, Yudi Che, Ling Lyu
    China Communications. 2022, 19(1): 153-165.
    Smart containers have been extensively applied in the maritime industry by embracing the Internet of Things to realize container status monitoring and data offloading without human intervention. However, the offloading rate and delay in the offshore region are limited by the coverage of the onshore base station (BS). In this paper, we investigate the unmanned aerial vehicle (UAV)-assisted data offloading for smart containers in offshore maritime communications where the UAV is as a relay node between smart containers and onshore BS. We first consider the mobility of container vessel in the offshore region and establish a UAV-assisted data offloading model. Based on this model, a data offloading algorithm is proposed to reduce the average offloading delay under data-size requirements and available energy constraints of smart containers. Specifically, the convex-concave procedure is used to update time-slot assignment, offloading approach selection, and power allocation in an iterative manner. Simulation results show that the proposed algorithm can efficiently reduce average offloading delay and increase offloading success ratio. Moreover, it is shown that the UAV relay cannot always bring the performance gain on offloading delay especially in the close-to-shore area, which could give an insight on the deployment of UAV relay in offshore communications.
  • Guest Editorial
    Xiaohan Qi, Zhihua Yang
    China Communications. 2022, 19(1): 136-152.
    This work focuses on an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system based on device-to-device (D2D) communication. In this system, the UAV exhibits caching, computing and relaying capabilities to periodically provide specific service to cellular users and D2D receiver nodes in the appointed time slot. Besides, the D2D transmitter can provide additional caching services to D2D receiver to reduce the pressure of the UAV. Note that communication between multi-type nodes is mutually restricted and different links share spectrum resources. To achieve an improved balance between different types of node, we aim to maximize the overall energy efficiency while satisfying the quality-of-service requirements of the cellular nodes. To address this problem, we propose an alternating iteration algorithm to jointly optimize the scheduling strategies of the user, transmitting power of the UAV and D2D-TX nodes, and UAV trajectory. The successive convex approximation, penalty function, and Dinkelbach method are employed to transform the original problem into a group of solvable subproblems and the convergence of the method is proved. Simulation results show that the proposed scheme performs better than other benchmark algorithms, particularly in terms of balancing the tradeoff between minimizing UAV energy consumption and maximizing throughput.
  • Guest Editorial
    Wanli Wen, Yunjian Jia, Wenchao Xia
    China Communications. 2022, 19(1): 119-135.
    Micro-UAV swarms usually generate massive data when performing tasks. These data can be harnessed with various machine learning (ML) algorithms to improve the swarm's intelligence. To achieve this goal while protecting swarm data privacy, federated learning (FL) has been proposed as a promising enabling technology. During the model training process of FL, the UAV may face an energy scarcity issue due to the limited battery capacity. Fortunately, this issue is potential to be tackled via simultaneous wireless information and power transfer (SWIPT). However, the integration of SWIPT and FL brings new challenges to the system design that have yet to be addressed, which motivates our work. Specifically, in this paper, we consider a micro-UAV swarm network consisting of one base station (BS) and multiple UAVs, where the BS uses FL to train an ML model over the data collected by the swarm. During training, the BS broadcasts the model and energy simultaneously to the UAVs via SWIPT, and each UAV relies on its harvested and battery-stored energy to train the received model and then upload it to the BS for model aggregation.To improve the learning performance, we formulate a problem of maximizing the percentage of scheduled UAVs by jointly optimizing UAV scheduling and wireless resource allocation.The problem is a challenging mixed integer nonlinear programming problem and is NP-hard in general. By exploiting its special structure property, we develop two algorithms to achieve the optimal and suboptimal solutions, respectively. Numerical results show that the suboptimal algorithm achieves a near-optimal performance under various network setups, and significantly outperforms the existing representative baselines.considered.
  • Guest Editorial
    Wenjing You, Chao Dong, Qihui Wu, Yuben Qu, Yulei Wu, Rong He
    China Communications. 2022, 19(1): 104-118.
    This paper establishes a new layered flying ad hoc networks (FANETs) system of mobile edge computing (MEC) supported by multiple UAVs, where the first layer of user UAVs can perform tasks such as area coverage, and the second layer of MEC UAVs are deployed as flying MEC sever for user UAVs with computing-intensive tasks. In this system, we first divide the user UAVs into multiple clusters, and transmit the tasks of the cluster members (CMs) within a cluster to its cluster head (CH). Then, we need to determine whether each CH' tasks are executed locally or offloaded to one of the MEC UAVs for remote execution (i.e., task scheduling), and how much resources should be allocated to each CH (i.e., resource allocation), as well as the trajectories of all MEC UAVs. We formulate an optimization problem with the aim of minimizing the overall energy consumption of all user UAVs, under the constraints of task completion deadline and computing resource, which is a mixed integer non-convex problem and hard to solve. We propose an iterative algorithm by applying block coordinate descent methods. To be specific, the task scheduling between CH UAVs and MEC UAVs, computing resource allocation, and MEC UAV trajectory are alternately optimized in each iteration. For the joint task scheduling and computing resource allocation subproblem and MEC UAV trajectory subproblem, we employ branch and bound method and continuous convex approximation technique to solve them, respectively. Extensive simulation results validate the superiority of our proposed approach to several benchmarks.
  • Guest Editorial
    Shijie Li, Qianyun Zhang, Boyu Deng, Biyi Wu, Yue Gao
    China Communications. 2022, 19(1): 92-103.
    This paper focuses on the trusted vessel position acquisition using passive localization based on the booming low-earth-orbit (LEO) satellites. As the high signal-to-noise ratio (SNR) reception cannot always be guaranteed at LEO satellites, the recently developed direct position determination (DPD) is adopted.For LEO satellite-based passive localization systems, an efficient DPD is challenging due to the excessive exhaustive search range leading from broad satellite coverage.In order to reduce the computational complexity, we propose a time difference of arrival-assisted DPD (TA-DPD) which minimizes the searching area by the time difference of arrival measurements and their variances.In this way, the size of the searching area is determined by both geometrical constraints and qualities of received signals, and signals with higher SNRs can be positioned more efficiently as their searching areas are generally smaller.Both two-dimensional and three-dimensional passive localization simulations using the proposed TA-DPD are provided to demonstrate its efficiency and validity.The superior accuracy performance of the proposed method, especially at low SNRs conditions, is also verified through the comparison to conventional two-step methods. Providing a larger margin in link budget for satellite-based vessel location acquisition, the TA-DPD can be a competitive candidate for trusted marine location service.
  • Guest Editorial
    Yuanzhi He, Biao Sheng, Hao Yin, Di Yan, Yingchao Zhang
    China Communications. 2022, 19(1): 77-91.
    Resource allocation is an important problem influencing the service quality of multi-beam satellite communications. In multi-beam satellite communications, the available frequency bandwidth is limited, users requirements vary rapidly, high service quality and joint allocation of multi-dimensional resources such as time and frequency are required. It is a difficult problem needs to be researched urgently for multi-beam satellite communications, how to obtain a higher comprehensive utilization rate of multi-dimensional resources, maximize the number of users and system throughput, and meet the demand of rapid allocation adapting dynamic changed the number of users under the condition of limited resources, with using an efficient and fast resource allocation algorithm. In order to solve the multi-dimensional resource allocation problem of multi-beam satellite communications, this paper establishes a multi-objective optimization model based on the maximum the number of users and system throughput joint optimization goal, and proposes a multi-objective deep reinforcement learning based time-frequency two-dimensional resource allocation (MODRL-TF) algorithm to adapt dynamic changed the number of users and the timeliness requirements. Simulation results show that the proposed algorithm could provide higher comprehensive utilization rate of multi-dimensional resources, and could achieve multi-objective joint optimization, and could obtain better timeliness than traditional heuristic algorithms, such as genetic algorithm (GA) and ant colony optimization algorithm (ACO).
  • Guest Editorial
    Min Sheng, Di Zhou, Weigang Bai, Junyu Liu, Jiandong Li
    China Communications. 2022, 19(1): 64-76.
    The rapid development and continuous updating of the mega satellite constellation (MSC) have brought new visions for the future 6G coverage extension, where the global seamless signal coverage can realize ubiquitous services for user terminals. However, global traffic demands present non-uniform characteristics. Therefore, how to ensure the on-demand service coverage for the specific traffic demand, i.e., the ratio of traffic density to service requirement per unit area, is the core issue of 6G wireless coverage extension exploiting the MSC. To this regard, this paper first discusses the open challenges to reveal the future direction of 6G wireless coverage extension from the perspective of key factors affecting service coverage performance, i.e., the network access capacity, space segment capacity and their matching-relationship. Furthermore, we elaborate on the key factors affecting effective matchings of the aforementioned aspects, thereby improving service coverage capability.
  • Guest Editorial
    Mingqian Liu, Chunheng Liu, Ming Li, Yunfei Chen, Shifei Zheng, Nan Zhao
    China Communications. 2022, 19(1): 52-63.
    Passive detection of moving target is an important part of intelligent surveillance. Satellite has the potential to play a key role in many applications of space-air-ground integrated networks (SAGIN). In this paper, we propose a novel intelligent passive detection method for aerial target based on reservoir computing networks. Specifically, delayed feedback networks are utilized to refine the direct signals from the satellite in the reference channels. In addition, the satellite direct wave interference in the monitoring channels adopts adaptive interference suppression using the minimum mean square error filter. Furthermore, we employ decoupling echo state networks to predict the clutter interference in the monitoring channels and construct the detection statistics accordingly. Finally, a multilayer perceptron is adopted to detect the echo signal after interference suppression. Extensive simulations is conducted to evaluate the performance of our proposed method. Results show that the detection probability is almost 100% when the signal-to-interference ratio of echo signal is -36dB, which demonstrates that our proposed method achieves efficient passive detection for aerial targets in typical SAGIN scenarios.
  • Guest Editorial
    Ziyong Li, Yuxiang Hu, Di Zhu, Jiangxing Wu, Yunjie Gu
    China Communications. 2022, 19(1): 40-51.
    The Space-Air-Ground Integrated Network (SAGIN) realizes the integration of space, air, and ground networks, obtaining the global communication coverage. Software-Defined Networking (SDN) architecture in SAGIN has become a promising solution to guarantee the Quality of Service (QoS). However, the current routing algorithms mainly focus on the QoS of the service, rarely considering the security requirement of flow. To realize the secure transmission of flows in SAGIN, we propose an intelligent flow forwarding scheme with endogenous security based on Mimic Defense (ESMD-Flow). In this scheme, SDN controller will evaluate the reliability of nodes and links, isolate malicious nodes based on the reliability evaluation value, and adapt multipath routing strategy to ensure that flows are always forwarded along the most reliable multiple paths. In addition, in order to meet the security requirement of flows, we introduce the programming data plane to design a multi-protocol forwarding strategy for realizing the multi-protocol dynamic forwarding of flows. ESMD-Flow can reduce the network attack surface and improve the secure transmission capability of flows by implementing multipath routing and multi-protocol hybrid forwarding mechanism. The extensive simulations demonstrate that ESMD-Flow can significantly improve the average path reliability for routing and increase the difficulty of network eavesdropping while improving the network throughput and reducing the average packet delay.
  • Guest Editorial
    Shuxun Li, Qian Chen, Zhe Li, Weixiao Meng, Cheng Li
    China Communications. 2022, 19(1): 29-39.
    In this paper, we propose a novel AI-enabled space-air-ground integrated networks (SAGIN). This new integrated networks architecture consists of LEO satellites and civil aircrafts carrying aerial base stations, called "civil aircraft assisted SAGIN (CAA-SAGIN)''. The assistance of civil aircrafts can reduce the stress of satellite networks, improve the performance of SAGIN, decrease the construction cost and save space resources. Taking the Chinese mainland as an example, this paper has analyzed the distribution of civil aircrafts, and obtained the coverage characteristics of civil aircraft assisted networks (CAAN). Taking Starlink as the benchmark, this paper has calculated the service gap of CAAN, and designed the joint coverage constellation. The simulation results prove that the number of satellites in CAA-SAGIN can be greatly reduced with the assistance of civil aircrafts at the same data rate.
  • Guest Editorial
    Xiaoyun Wang, Tao Sun, Xiaodong Duan, Dan Wang, Yongjing Li, Ming Zhao, Zhigang Tian
    China Communications. 2022, 19(1): 14-28.
    The Service-based Architecture (SBA) is one of the key innovations of 5G architecture that leverage modularized, self-contained and independent services to provide flexible and cloud-native 5G network. In this paper, SBA for Space-Air-Ground Integrated Network (SAGIN) is investigated to enable the 5G integration deployment. This paper proposes a novel Holistic Service-based Architecture (H-SBA) for SAGIN of 5G-Advanced and beyond, i.e., 6G. The H-SBA introduces the concept of end-to-end service-based architecture design. The “Network Function Service”, introduced in 5G SBA, is extended from Control Plane to User Plane, from core network to access network. Based on H-SBA, the new generation of protocol design is proposed, which proposes to use IETF QUIC and SRv6 to substitute 5G HTTP/2.0 and GTP-U. Testing results show that new protocols can achieve low latency and high throughput, making them promising candidate for H-SBA.
  • Guest Editorial
    Linhui Wei, Jiacheng Shuai, Yu Liu, Yumei Wang, Lin Zhang
    China Communications. 2022, 19(1): 1-13.
    The space-air-ground integrated network (SAGIN) is regarded as the key approach to realize global coverage in future network and it reaches broad access for various services. Being the new paradigm of service, immersive media (IM) has attracted users' attention for its virtualization, but it poses challenges to network performance, e.g. bandwidth, rate, latency. However, the SAGIN has limitations in supporting IM services, such as 4K/8K video, virtual reality, and interactive games. In this paper, a novel service customized SAGIN architecture for IM applications (SAG-IM) is proposed, which achieves content interactive and real-time communication among terminal users. State-of-the-art research is investigated in detail to facilitate the combination of SAGIN and service customized technology, which provides end-to-end differentiated services for users. Besides, the functional components of SAG-IM contain the infrastructure layer, perception layer, intelligence layer, and application layer, reaching the capabilities of intelligent management of the network. Moreover, to provide IM content with ultra-high-definition and high frame rate for the optimal user experience, the promising key technologies on intelligent routing and delivery are discussed. The performance evaluation shows the superiority of SAG-IM in supporting IM service. Finally, the prospects in practical application are highlighted.