The convergence of terrestrial and non-terrestrial (e.g., satellite communications) infrastructures has been attracting more and more attention from connectivity stakeholders given the forthcoming future sixth-generation (6G) communication paradigm, thanks to ubiquitous coverage and broadcast/multicast capabilities offered by SatCom. With the advent of promising concepts such as the Internet of Space Things (IoST), the potential for applying edge computing in space has become particularly appealing. In particular, in-orbit computing services are provided employing edge servers placed on satellites, which can inherently offer storage and computation capacity. However, an open problem is to find out onboard which satellite edge servers should be placed and how distributed edge computing services could be offered. In this respect, this paper proposes satellite-terrestrial integrated edge computing networks (STECN), aiming at minimizing the average response latency and average energy consumption by jointly selecting the optimal deployment of servers and services. First, we propose a general network framework for the STECN. Then, we develop a heuristic algorithm based on K-medoids clustering and non-dominated sorting genetic algorithm-II (NSGA-II) to solve the optimization problem. Specifically, we achieve the optimal placement of edge servers by exploiting the K-medoids clustering method. In terms of service deployment, the conducted performance analysis shows that the proposed solutions can approach the Pareto Front using the NSGA-II technique. Finally, the proposed heuristic algorithm is compared with two benchmark algorithms by means of simulations, verifying the effectiveness of our proposed algorithm. To the best of our knowledge, this paper is the first work considering server placement and service deployment in STECN.

Inter-satellite links (ISLs) are adopted in global navigation satellite systems (GNSSs) for high-precision orbit determination and space-based end-to-end telemetry telecommand control and communications. Due to limited onboard ISL terminals, the polling time division duplex (PTDD) mechanism is usually proposed for space link layer networking. By extending the polling mechanism to ground-satellite links (GSLs), a unified management system of the space segment and the ground segment can be realized. However, under the polling system how to jointly design the topology of ISLs and GSLs that are both time-slotted to improve data interaction has not been studied. In this paper, we formulate the topology design problem as an integer linear programming problem, aiming at minimizing the average delay of data delivery from satellites to ground stations while satisfying the ranging requirement for orbit determination. To tackle the computational complexity problem, we first present a novel modeling method of delay to reduce the number of decision variables. Furthermore, we propose a more efficient heuristic based on maximum weight matching algorithms. Simulation results demonstrate the feasibility of the proposed methods for practical operation in GNSSs with time-slotted GSLs. Comparing the two methods, the heuristic can achieve similar performance with respect to average delay but with significantly less complexity.

Next generation Global Navigation Satellite Systems (GNSSs) will embrace Inter-Satellite Links (ISLs) capability as an enabler of navigation and data transfer. However, platform restrictions will require of suitable Contact Plan Design (CPD) schemes which has been traditionally assumed centralized. After discussing the requirements of distributed CPD, we propose a first scheme in this class. Simulation results on the BeiDou GNSS prove that satisfactory metrics can be obtained enabling valuable and real autonomy for future GNSSs.

To build the new generation of ubiquitous communication and service integration networks with “network omnipresence and computing ubiquitous,” it is urgent to improve the in-orbit computing ability of low earth orbit (LEO) constellation networks and develop intelligent technology for satellite–ground collaborative edge computing. Communication tasks between ground nodes and satellites are increasing, but the satellite-to-ground spectrum resources are limited. The reasonable application of channels determines the performance of the network, which in turn affects the users’ experience. An outstanding issue is how to allocate channels rationally and control the power of data transmission to reduce co-channel interference and minimize system overhead effectively. This article studies multiuser computation offloading for low earth orbit (LEO) constellation networks under dynamic environment, wherein the system overhead is minimized by joint offloading strategy and power optimization. First, we propose a generic network architecture for computation offloading of LEO constellation networks under the dynamic environment. Then, from a game-theoretic perspective, we model the overhead minimization problem as a potential game and prove that the Nash equilibrium (NE) minimizes the system overhead. After that, to reach the NE, we design the Synchronous log-linear learning-based power control algorithm and joint offloading strategy and power optimization algorithm based on SLA (JOPAS), and prove the convergence of the algorithms. Finally, the effectiveness of the proposed algorithm is verified through extensive simulations and comparisons with benchmark algorithms, and the proposed algorithm achieves near-optimal performance.

Internet of Space Things (IoST), which extends the concept of Internet of Things (IoT) to space, has emerged as a new paradigm for offering monitoring/reconnaissance, in-space backhaul, and cyber-physical integration services. As Low Earth Orbit (LEO) satellites are increasingly deployed for global Internet services, Mobile Edge Computing (MEC) is being introduced into satellite networks to provision computing services by placing edge servers on satellites. Nevertheless, it is a nontrivial and unexplored task to efficiently choose edge server deployment locations from a large number of satellites. In this paper, we address this issue in detail towards average response delay minimization considering propagation delay, forwarding delay, and service delay. In particular, we formulate the dynamic server placement problem as well as the static server placement problem, and devise a genetic algorithm-based heuristic approach to solve them. Simulation results compare the two placement strategies with two benchmarks and demonstrate the performance of our genetic algorithm-based approach. Furthermore, a comparison between the dynamic placement and the static placement is investigated.

In this paper, we investigate the resource management for quality of experience (QoE)-centric low earth orbit (LEO) satellite-terrestrial integrated network (STIN) based on non-orthogonal multiple access (NOMA). In satellite communication, existing efforts are devoted to maximizing quality of service (QoS) metrics, such as throughput. The optimization mechanism centered around QoS may to some extent impact the fairness of the system, leading to a decrease in resource utilization. To achieve more rational resource allocation, we focus on maximizing the user's QoE. We apply NOMA to LEO STIN to improve spectral efficiency and utilize the mean opinion score (MOS) to assess user satisfaction. Then, we maximize the sum MOSs of all users by jointly optimizing channel access, user pairing and power allocation. However, the proposed problem is a complex non-convex problem that is challenging to solve directly. To tackle this issue, we propose a coalition formation algorithm based on better response for user pairing and channel access. The power allocation is performed by deriving and presenting a linear search algorithm. Simulation results show that the proposed QoE optimization framework can effectively improve the QoE performance of the system compared with the QoS-centric optimization scheme and the OMA transmission method. Furthermore, in contrast to existing work, the QoE optimization proposed in this paper enhances the fairness of the system and increases resource utilization without incurring additional system costs.