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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]

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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.

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]

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.

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.