Space-Air-Ground Integrated Network (SAGIN) for 6G: Requirements, Architecture and Challenges

As the fifth-generation(5G)mobile communication network may not meet the requirements of emerging technologies and applications,including ubiquitous coverage,industrial internet of things(IIoT),ubiquitous artificial intelligence(AI),digital twins(DT),etc.,this paper aims to explore a novel space-air-ground integrated network(SAGIN)architecture to support these new requirements for the sixth-generation(6G)mobile communication network in a flexible,low-latency and efficient manner.Specifically,we first review the evolution of the mobile communication network,followed by the application and technology requirements of 6G.Then the current 5G non-terrestrial network(NTN)architecture in supporting the new requirements is deeply analyzed.After that,we proposes a new flexible,low-latency and flat SAGIN architecture,and presents corresponding use cases.Finally,the future research directions are discussed.

Characterizing Flight Delay Profiles with a Tensor Factorization Framework

In air traffic and airport management,experience gained from past operations is crucial in designing appropriate strategies when facing a new scenario.Therefore,this paper uses massive spatiotemporal flight data to identify similar traffic and delay patterns,which become critical for gaining a better understanding of the aviation system and relevant decision-making.However,as the datasets imply complex dependence and higher-order interactions between space and time,retrieving significant features and patterns can be very challenging.In this paper,we propose a probabilistic framework for highdimensional historical flight data.We apply a latent class model and demonstrate the effectiveness of this framework using air traffic data from 224 airports in China during 2014–2017.We find that profiles of each dimension can be clearly divided into various patterns representing different regular operations.To prove the effectiveness of these patterns,we then create an estimation model that provides preliminary judgment on the airport delay level.The outcomes of this study can help airport operators and air traffic managers better understand air traffic and delay patterns according to the experience gained from historical scenarios.

Measuring air traffic complexity based on small samples

Air traffic complexity is an objective metric for evaluating the operational condition of the airspace. It has several applications, such as airspace design and traffic flow management.Therefore, identifying a reliable method to accurately measure traffic complexity is important. Considering that many factors correlate with traffic complexity in complicated nonlinear ways,researchers have proposed several complexity evaluation methods based on machine learning models which were trained with large samples. However, the high cost of sample collection usually results in limited training set. In this paper, an ensemble learning model is proposed for measuring air traffic complexity within a sector based on small samples. To exploit the classification information within each factor, multiple diverse factor subsets（FSSs） are generated under guidance from factor noise and independence analysis. Then, a base complexity evaluator is built corresponding to each FSS. The final complexity evaluation result is obtained by integrating all results from the base evaluators. Experimental studies using real-world air traffic operation data demonstrate the advantages of our model for small-sample-based traffic complexity evaluation over other stateof-the-art methods.

Motion Model of Floating Weather Sensing Node for Typhoon Detection

To improve the accuracy of typhoon prediction,it is necessary to detect the internal structure of a typhoon.The motion model of a floating weather sensing node becomes the key to affect the channel frequency expansion performance and communication quality.This study proposes a floating weather sensing node motion modeling method based on the chaotic mapping.After the chaotic attractor is obtained by simulation,the position trajectory of the floating weather sensing node is obtained by space and coordinate conversion,and the three-dimensional velocity of each point on the position trajectory is obtained by multidimensional linear interpolation.On this basis,the established motion model is used to study the Doppler frequency shift,which is based on the software and physical platform.The software simulates the relative motion of the transceiver and calculates the Doppler frequency shift.The physical platform can add the Doppler frequency shift to the actual transmitted signal.The results show that this method can effectively reflect the influence of the floating weather sensing node motion on signal transmission.It is helpful to research the characteristics of the communication link and the design of a signal transceiver for typhoon detection to further improve the communication quality and to obtain more accurate interior structure characteristic data of a typhoon.

Vulnerability analysis of the Chinese coupled aviation and high-speed railway network

The integrated aviation and High-Speed Railway(HSR)transportation system plays a vital role for today’s inter-city transportation services.However,an increasing number of unexpected disruptions(such as operation failures,natural disasters,or intentional attacks)pose a considerable threat to the normal operation of the system,especially on ground transfer,leading to the extensive research on its vulnerability.Previous approaches mainly focus on interruptions within a single transportation mode,neglecting the role of ground transfer which serves as a coupled connection between aviation and High-Speed Railway.This paper proposes a network-based framework for evaluating the vulnerability of the Chinese Coupled Aviation and High-Speed Railway(CAHSR)network from the viewpoint of ground transfer interruption.Taking the end-to-end travel time and passenger flow information into consideration as an evaluation measure and analyzing from the perspective of urban agglomerations,an adaptive method is developed to identify the critical cities and further investigate their failure impacts on the geographic distribution of vulnerability.In addition,the proposed model explores variations of vulnerability under different failure time intervals.Based on the empirical study,some major conclusions are highlighted as follows:(A)Only a few cities show significant impacts on the network’s vulnerability when ground transfer interruptions occurred.(B)The distribution of vulnerability is not proportional to the distance between failure city and influenced city.(C)The vulnerability is more serious in the morning and evening when the ground transfer is disconnected.Our findings may provide new insights for maintenance and optimization of the CAHSR network and other real-world transportation networks.

Special Issue call for papers: Emerging Technologies of Unmanned Aerial Vehicles

Topic Summary: The past few years have witnessed the rapid development of unmanned aerial vehicles (UAVs). To better meet the increasing demands of real applications, UAVs are expected to support intelligent autonomous control, real-time navigation and surveillance, and broadband airborne communication.