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Facing the future vision of ubiquitous network coverage, the deep integration and cooperative networking between satellite and terrestrial mobile communication network is a necessary technical path to build a space-air-ground integrated network, which can support access at anytime and anywhere. Firstly, current situation and development trend of space-air-ground integrated network were introduced. Then, the network architecture of satellite-terrestrial network and the design of space-based simplified core network were discussed, as well as the key technologies of satellite-terrestrial network. Finally, the proposed network architecture was verified by relevant experiments. and the follow-up research plan and outlook for future directions were discussed.

With the widespread deployment of dense Low Earth Orbit (LEO) constellations, satellites can serve as an alternative solution to the lack of proximal multi-access edge computing (MEC) servers for mobile Internet of Things (IoT) devices in remote areas. Simultaneously, leveraging federated learning (FL) to address data privacy concerns in the context of satellite-terrestrial cooperative IoT is a prudent choice. However, in the traditional satellite-ground FL framework where model aggregation occurs solely on satellite onboard terminals, challenges of insufficient satellite computational resources and congested core networks are encountered. Hence, we propose a cost-efficient satellite-terrestrial assisted hierarchical federated edge learning (STA-HFEL) architecture in which the satellite edge server performs as intermediaries for partial FL aggregation between IoT devices and the remote cloud. We further introduced an innovative communication scheme between satellites based on Intra-plane ISLs in this paper. Accordingly, considering the resource constraints of battery-limited devices, we define a joint computation and communication resource optimization problem for device users to achieve global cost minimization. By decomposing it into local training computational resource allocation subproblem and local model uploading communication resource subproblem, we used a distributed Jacobi-Proximal ADMM (JPADMM) algorithm to tackle the formulated problem iteratively. Extensive performance evaluations demonstrate that the potential of STA-HFEL as a cost-efficient and privacy-preserving approach for machine learning tasks across distributed remote environments. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Satellite-Terrestrial integrated Networks (STNs) have been advocated by both academia and industry as a promising network paradigm to achieve service continuity and ubiquity. However, STNs suffer from problems including poor flexibility of network architecture, low adaptability to dynamic environments, the lack of network intelligence, and low resource utilization. To handle these challenges, a Software defined Intelligent STN (SISTN) architecture is introduced. Specifically, the hierarchical architecture of the proposal is described and a distributed deployment scheme for SISTNs controllers is proposed to realize agile and effective network management and control. Moreover, three use cases in SISTNs are discussed. Meanwhile, key techniques and their corresponding solutions are presented, followed by the identification of several open issues in SISTNs including compatibility with existing networks, the tradeoff between network flexibility and performance, and so on.

Leveraging technological advancements such as containers, microservices, and service mesh, cloud-native edge computing (CNEC) has become extensively discussed and applied in both academia and industry. The integration of mobile edge computing and communication is crucial for the future communication architecture in order to fully utilize distributed and fragmented communication resources and computing power. The potential for cloud-native integration can help merge mobile edge computing and communication, enhancing network flexibility and resource utilization. This paper investigates the implementation plan for extending cloud-native capabilities to integrated computing and communication (INCCOM) in the satellite–terrestrial network. We construct an experimental verification platform called ComEdge in a real-world setting. Subsequently, we analyze the architecture, functional characteristics, and deployment of the platform in a real-world environment. Furthermore, we explore the solution of deep reinforcement learning in the deployment of cloud-native core network and conduct a preliminary verification of the platform's potential to enable artificial intelligence in a real production environment, which will provide guidance to both academic and industry sectors. Finally, we conduct an analysis on the challenges and opportunities encountered by the cloud-native INCCOM network system. [ABSTRACT FROM AUTHOR]

Edge cloud at satellite (ECS) is a newly developed edge computing (EC) technology that uses EC services offered by satellites to support high reliability and seamless global coverage. Satellites assume the role of computing and storage nodes for edge clouds, while terrestrial control centers function as cloud centers. In this paper, we propose a novel system and software architecture for the ECS to improve the cloud management of satellite networks and increase the flexibility of satellite service provision at the edge. Then, we propose a platform for the ECS based on KubeEdge called SatEdge. SatEdge has many function modules to meet the needs of the satellite-terrestrial network (STN) such as high reliability, high flexibility, and low latency. On this platform, we designed a microservice scheduling algorithm called optimal microservice scheduling with adaptivity and mobility (OMS-AM). OMS-AM can schedule a globally optimal workflow for microservice modules on the satellites to minimize task processing latency, failed task rate, and energy consumption. Compared with our last work, OMS-AM reduces the task processing latency by 14% at most. Additionally, OMS-AM improves the mobility of the current scheduling method put forth in our previous study, which may help lower the task failure rate. Energy usage and the total normalized costs are additional indicators of the efficiency of the microservice architecture.

Considering the spatial possibility of opportunistic spectrum sharing, sensing-based spectrum sharing method ws applied in the satellite-terrestrial network.The sensing-throughput tradeoff problem was studied, and the throughput is a concave function of the sensing duration was proved.In order to further improved the satellite uplink throughput, the multi-slots sensing method and the cooperative sensing method were proposed.Computer simulations showed that the optimal sensing duration could maximized the uplink throughput, and the multi-slot sensing method or the cooperative sensing method could further improved the throughput of satellite network.

With the development of communication systems, users are becoming more widely distributed and require higher speed networks. A satellite–terrestrial integrated network could provide seamless coverage for these users. In previous studies of load balancing, initial access and load balancing are decided on based on signal reception and are performed reactively after the overloading occurs, which may not work well in satellite–terrestrial integrated networks. Therefore, this paper proposes a fuzzy-logic-based load balancing scheme. In this scheme, a fuzzy evaluation metric to pre-evaluate the user's impact on overload is presented. The fuzzy logic system is constructed based on adaptive neuro fuzzy system, which takes the user's signal reception, speed and data requirement as inputs. Then, the fuzzy-logic- and reinforcement-learning-based access is proposed to give an access decision for all users in the network to prevent overloading. Due to the large dimensions of action space, the reinforcement learning model is trained by the proposed fuzzy, deep, deterministic policy gradient. Next, the fuzzy-logic-based offloading algorithm is proposed to balance load after overloading. A simulation platform is established to evaluate the performance. Simulation results indicate that the proposed scheme can ensure load balance for a longer time than base line schemes while ensuring data rate of users. [ABSTRACT FROM AUTHOR]

Aiming at providing a high-performance terrestrial network for edge computing in satellite networks, we experimentally demonstrate a high bandwidth and low latency decomposed optical computing architecture based on distributed Nanoseconds Optical Switches (NOS). Experimental validation of the decomposed computing network prototype employs a four-port NOS to interconnect four processor/memory cubes. The SOA-based optical gates provide an ON/OFF ratio greater than 60 dB, enabling none-error transmission at a Bit Error Rate (BER) of 1 × 10−9. An end-to-end access latency of 122.3 ns and zero packet loss are obtained in the experimental assessment. Scalability and physical performance considering signal impairments when increasing the NOS port count are also investigated. An output OSNR of up to 30.5 dB and an none-error transmission with 1.5 dB penalty is obtained when scaling the NOS port count to 64. Moreover, exploiting the experimentally measured parameters, the network performance of NOS-based decomposed computing architecture is numerically assessed under larger network scales. The results indicate that, under a 4096-cube network scale, the NOS-based decomposed computing architecture achieves 148.5 ns end-to-end latency inside the same rack and zero packet loss at a link bandwidth of 40 Gb/s. [ABSTRACT FROM AUTHOR]

To improve users’ communication experience when they access satellite-terrestrial networks, we propose a long-short term bandwidth allocation strategy for beam-hopping low-earth-orbit satellites. Our strategy consists of two collaboratively functioned parts, the prediction-based long-term (PLT) bandwidth allocation and the trend-based short-term (TST) bandwidth allocation. At the beginning of each long term, the PLT method allocates bandwidth resources according to the maximum traffic volume predicted by Hull moving average (HMA), and the Q-learning assisted TST method adjusts the bandwidth allocation of the last short term with consideration of the traffic trend when necessary. Simulation results demonstrate that the proposed strategy is lightweight and can effectively reduce the blocking ratio and improve the supply-demand ratio of satellite-terrestrial communications. [ABSTRACT FROM AUTHOR]

With the development of satellite-terrestrial network (STN), mobile edge computing (MEC) servers are deployed at low orbit earth (LEO) satellites to provide computing services for user devices (UEs) in areas without terrestrial network coverage. There is symmetry between satellite networks and terrestrial networks, but there is asymmetry between their resources. Computing resources of satellites' MEC servers may not be enough. The satellite-terrestrial cooperation is promising, where a satellite migrates tasks to a base station (BS) in an adjacent area, thus utilizing computing resources of the BS's MEC server. Although there are some studies on computation offloading in STN, few studies consider a satellite as both a relay and a computing unit to assist UEs in computing tasks. This paper proposes a joint offloading decision and resource allocation scheme in MEC-enabled STN, which minimizes the completion delay of all UEs' indivisible tasks. Firstly, the optimization problem is formulated and decomposed. Then, the proposed scheme based on potential game and the Lagrange multiplier method makes UEs' task offloading decisions and allocates the satellite's and the BS's computing resources, thus obtaining the optimal solution through continuous iterations. Finally, the simulation results validate that the proposed scheme can obtain better gain than other baseline schemes. [ABSTRACT FROM AUTHOR]

The introduction of mobile edge computing (MEC) technology in satellite-terrestrial networks can effectively improve the quality of user experience and reduce network redundant traffic,but also brings some challenges.Firstly,the basic architecture of satellite-terrestrial networks and MEC technology was introduced.Moreover,the motivation of introducing MEC into satellite-terrestrial networks and the deployment of MEC were discussed.Then,the architecture of MEC enabled satellite-terrestrial networks was proposed,and the key techniques and typical applications were summarized and analyzed.Finally,the key challenges such as task scheduling and mobility management and some open research issues in integrated networks were summarized.It hopes to provide new ideas for future research in this field.

Copyright of Space-Integrated-Ground Information Networks is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at providing a high-performance terrestrial network for edge computing in satellite networks, we experimentally demonstrate a high bandwidth and low latency decomposed optical computing architecture based on distributed Nanoseconds Optical Switches (NOS). Experimental validation of the decomposed computing network prototype employs a four-port NOS to interconnect four processor/memory cubes. The SOA-based optical gates provide an ON/OFF ratio greater than 60 dB, enabling none-error transmission at a Bit Error Rate (BER) of 1 × 10−9. An end-to-end access latency of 122.3 ns and zero packet loss are obtained in the experimental assessment. Scalability and physical performance considering signal impairments when increasing the NOS port count are also investigated. An output OSNR of up to 30.5 dB and an none-error transmission with 1.5 dB penalty is obtained when scaling the NOS port count to 64. Moreover, exploiting the experimentally measured parameters, the network performance of NOS-based decomposed computing architecture is numerically assessed under larger network scales. The results indicate that, under a 4096-cube network scale, the NOS-based decomposed computing architecture achieves 148.5 ns end-to-end latency inside the same rack and zero packet loss at a link bandwidth of 40 Gb/s.

In recent years, mobile services have developed rapidly and traditional satellite-terrestrial networks have been unable to support them. We are faced with the problems of how to locate mobile terminals accurately and process the data we collected quickly to reduce communication pressure. In order to solve this problem, this paper studies a pointing and tracking method based on artificial intelligence for mobile stations and terminals in satellite-terrestrial network, to make sure that our mobile stations and terminals can access best antenna signal and suffer minimal communication interference from other stations or terminals. An AI-based self learning (ASL) network framework is designed to support filtering and correct original sampling data, mobile tracking of mobile stations and terminals, and unsupervised satellite selection and antenna adjustment scheme. Deep learning of historical information data of stations and terminals to achieve real-time pointing and tracking, and predict the distribution of stations and terminals at some time in the future. Finally, the ASL is compared with existing systems to measure their functionality and usability.

With the development of satellite-terrestrial network (STN), mobile edge computing (MEC) servers are deployed at low orbit earth (LEO) satellites to provide computing services for user devices (UEs) in areas without terrestrial network coverage. There is symmetry between satellite networks and terrestrial networks, but there is asymmetry between their resources. Computing resources of satellites’ MEC servers may not be enough. The satellite-terrestrial cooperation is promising, where a satellite migrates tasks to a base station (BS) in an adjacent area, thus utilizing computing resources of the BS’s MEC server. Although there are some studies on computation offloading in STN, few studies consider a satellite as both a relay and a computing unit to assist UEs in computing tasks. This paper proposes a joint offloading decision and resource allocation scheme in MEC-enabled STN, which minimizes the completion delay of all UEs’ indivisible tasks. Firstly, the optimization problem is formulated and decomposed. Then, the proposed scheme based on potential game and the Lagrange multiplier method makes UEs’ task offloading decisions and allocates the satellite’s and the BS’s computing resources, thus obtaining the optimal solution through continuous iterations. Finally, the simulation results validate that the proposed scheme can obtain better gain than other baseline schemes.

To improve the energy efficiency for satellite downlink, a data offloading mechanism is studied for multi-cell satellite-terrestrial networks. Different services are first offloaded to the satellite-terrestrial terminal (STT) in each small cell and then forwarded to grounded users. By integrating the power allocation for satellite backhauling and the resource allocation for STT downlink, we aim to maximize the energy efficiency of data offloading in the multi-cell satellite-terrestrial networks. The problem is decomposed into two types of subproblems, where the downlink capacity in each cell, as well as the energy efficiency of satellite backhauling, are maximized. The optimal solution is approached by invoking a binary search aided algorithm, and simulation results demonstrate that the proposed algorithm effectively achieves the preferred energy-efficient allocation. [ABSTRACT FROM AUTHOR]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the traditional satellite networks, network resources are mainly allocated among all the satellites based on the same allocation algorithm. This kind of symmetry model limits the increase of throughput. In this paper, we study an asymmetry resource allocation method in a satellite–terrestrial network and propose a Lotka–Volterra based predator–prey model to achieve optimal resource allocation among different satellites. In the proposed satellite–terrestrial network, we divide all the satellites into two groups, and we try to achieve load stability between these two satellites groups. Using the predator–prey model, one group is the prey–satellites, which can obtain service requirements from mobile users. The other group is considered as predator–satellites, which can only obtain the loads from the group of the prey–satellites. Once the satellites are divided into two groups using the Lotka–Volterra model, the resource allocation problem among these satellites in two groups would be asymmetry resource. We prove the existence of solutions to the proposed model. Numerical simulation results are given to show the correctness and effectiveness of the proposed model.

Convergence between wireless networks is the main trend in current and future wireless communications. The distinct advantage of satellite and terrestrial network integration is the possibility to provide ubiquitous multimedia services in vehicles at any location. The key design considerations of mobile broadband satellite access technologies are given in this article. After presenting the conceptual models of a system and services, the design issues of satellite network synchronization and burst demodulation are described. The design considerations of medium access control, resource management, capacity, and buffer controls for internetworking are given. Also, the active antenna issues are provided along with a sample design. [ABSTRACT FROM PUBLISHER]

In the traditional satellite networks, network resources are mainly allocated among all the satellites based on the same allocation algorithm. This kind of symmetry model limits the increase of throughput. In this paper, we study an asymmetry resource allocation method in a satellite–terrestrial network and propose a Lotka–Volterra based predator–prey model to achieve optimal resource allocation among different satellites. In the proposed satellite–terrestrial network, we divide all the satellites into two groups, and we try to achieve load stability between these two satellites groups. Using the predator–prey model, one group is the prey–satellites, which can obtain service requirements from mobile users. The other group is considered as predator–satellites, which can only obtain the loads from the group of the prey–satellites. Once the satellites are divided into two groups using the Lotka–Volterra model, the resource allocation problem among these satellites in two groups would be asymmetry resource. We prove the existence of solutions to the proposed model. Numerical simulation results are given to show the correctness and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]