Satellite edge computing networks are gaining momentum, necessitating efficient task scheduling and resource allocation to optimize performance. This paper focuses on collaborative computing among multiple edge satellites, addressing the dynamic nature of satellite networks and energy constraints. We propose the Delay-Optimal Task Scheduling Offloading and Resource Allocation (DO-TSO-RA) algorithm to minimize task processing delay and ensure network stability. By decomposing the problem into task scheduling, offloading, and resource allocation subproblems and employing alternating optimization and sequential task scheduling algorithm to solve them. Finally simulations demonstrates superior performance of DO-TSORA in reducing task delay and improving resource utilization compared to existing methods.

Inter-satellite links (ISLs) enable the dense satellite network to remarkably enhance the communication performance of the satellite system. However, the increasing number of LEO satellites and the widespread application of ISLs lead to severe inter-satellite interference (ISLI), restricting communication performance. Therefore, it is essential to study and further restrain the increasingly severe ISLI issue to make full use of the microwave spectrum resources. Meanwhile, considering the importance of satellite network to ground coverage, we construct the problem as a multi-objective optimization problem that maximizes inter satellite signal-to-noise ratio (SINR) and coverage rate. Furthermore, we adopt ε-constraint method to convert it into a single-objective optimization problem and propose a low-complexity maximum spatial uniformity constellation design method. Finally, the influencing factors of ISLI are analyzed through simulation, and the feasibility and effectiveness of the proposed method are verified.

The deployment of low-Earth orbit (LEO) constellations, combined with inter-satellite link (ISL) technology, will drive the development of future sixth-generation (6G) and satellite Internet of Things (SIoT). However, the deployment of a large number of LEO satellites and corresponding ISLs intensifies the spectrum resource scarcity in space, which may result in severe ISL interference (ISLI). Therefore, this paper proposes a theoretical framework for optimal design of LEO satellite constellations to mitigate ISLI. Specifically, the system average signal-to-interference-plus-noise ratio (SA-SINR) metric is introduced to depict ISLI, and deterministic analysis expression for SA-SINR is derived. Meanwhile, an integral merging algorithm (IMA) is proposed to accurately calculate the ground coverage area of satellite networks, considering the need to cover ground users in a wide distribution range. Then, the optimal constellation design issue is formulated into a mathematical multi-objective optimization problem, and an improved particle swarm optimization (PSO) scheme is designed to solve it. Simulation results show that the constellation designed based on the proposed approach outperforms the existing Starlink and OneWeb constellations in terms of ISLI and coverage performances, validating the effectiveness of our proposal.

Advancements in communication, manufacturing, and launch technologies have significantly accelerated the development of Low-Earth orbit (LEO) satellite communication constellations. Simultaneously, the rapid expansion of LEO satellite platforms presents new opportunities for navigation enhancements and substantial benefits for internet of things (IoT) applications. This survey highlights the latest advancements in the integrated communication and navigation (ICAN) technologies, emphasizing key research issues. Specifically, we discuss the motivation and feasibility of integrating communication and navigation on LEO satellite platforms, analyzing various LEO-based navigation paradigms while assessing their respective advantages and limitations. We further explore the mutual promotion of navigation and communication under the ICAN paradigm and discuss waveform design, analyzing the characteristics of different waveform design approaches. Finally, we highlight the unique challenges and identify open research topics within the ICAN framework for future exploration.

The advantages of Low Earth Orbit (LEO) satellite navigation in addressing the increasing demand for high-precision and universally accessible positioning have garnered widespread attention. Our work builds upon our previous development of the Zadoff-Chu non-orthogonal multiple access (ZC-NOMA) waveform for integrated communication and navigation (ICAN). Expanding on this foundation, we conceive the ICAN-oriented multi-satellite asynchronous transmission (ICAN-MSAT) framework, which allows users to concurrently receive communication and navigation signals from multiple satellites without synchronization, thus increasing communication flexibility and leveraging the geometric distribution and robust signals of LEO satellite constellations to enhance navigation performance. Within the ICAN-MSAT framework, we propose a novel multi-satellite asynchronous transmission oriented subcarrier and power allocation (MSASP) algorithm to manage mutual interference between communication and navigation components and inter-satellite interference (INSI) caused by asynchronous transmission, with the latter often overlooked in most existing work. Simulation results demonstrate that the proposed algorithm meets the performance requirements of both navigation and communication, achieving higher communication rates under the same navigation accuracy constraints compared to current benchmarks.

The underwater sensor network plays a critical role in the ocean data collection owing to its capability of providing reliable and wide communication coverage. How to assure the lifetime performance of the network with a limited energy supply has always been a key research issue. In this article, we propose an underwater acoustic sensor network model which supports data delivery from each underwater sensor node (USN) to the sea surface sink node (SN) in either direct or relay-assisted transmission mode, i.e., transmission mode between each USN and the SN can be dynamically selected according to network’s current state. To optimize its lifetime performance, we model the mode selection and resource allocation issues jointly into a nonconvex and mixed-integer programming problem. In order to efficiently solve it, we further divide the original problem into two subproblems: 1) resource allocation subproblem and 2) joint mode and relay selection subproblem. We prove that the reformulated nonconvex resource allocation problem can be equivalent to a convex optimization problem, and its optimal solution can be found by using Lagrange dual decomposition method. For the second subproblem, some critical conditions are analyzed to determine optimal relays with the criterion of balancing energy consumption between USNs, and a matching approach is designed to obtain the mode and relay selection results based on USNs’ priorities. Simulation results validate that the proposed transmission scheme is effective and superior to the traditional time division multiple access scheme.

In this paper, we investigate the collaborative computation offloading method in satellite edge computing by allowing computation tasks to be executed by multiple satellites with computing capacity. The main purpose is to optimize the resource allocation to minimize the energy consumption of the network, which is formulated as a non-convex optimization problem. To solve it efficiently, we first provide the optimal task allocation scheme and then divide the original optimization problem into two subproblems based on an alternative optimization method. Although two subproblems are still non-convex, we can apply successive convex approximation method to deal with them and design an iterative algorithm to solve them. Finally, simulation results demonstrate the superiority and effectiveness of our proposed algorithm.

This paper considered a data rate maximization problem of relay satellite network and aimed to overcome the difficulty of direct transmission between the satellites and ground station. The proposed optimization problem was first formulated as a mixed integer non-convex programming which is difficult to solve. Then, to design an efficient solving method, the proposed optimization problem were approximately decoupled into two subproblems including relay selection problem and power allocation problem. Through a node virtualization method, the relay selection problem was converted to a maximum weighted matching problem. However, the power allocation problem was still a non-convex problem but could be approximated as convex problem by using difference of convex (DC) programming. Moreover, an iterative algorithm was designed to acquire the optimal solution of approximated convex problem based on the Lagrangian dual method. Finally, simulation results are provided to demonstrate the superiority of the algorithm.

In this article, we present an unmanned aerial vehicle (UAV)-aided ocean monitoring network for remote oceanic data collection, in which monitoring data are transmitted first from battery-powered underwater sensor nodes (USNs) to sea surface sink nodes (SNs) in a data collection cycle using underwater acoustic communication, and then a UAV hovering in air collects all the data from SNs and relays them to a ground base station via wireless communication links. Aiming at maximizing network lifetime, we model the resource allocation, USN-to-SN access, and SN-to-UAV access issues as a mixed-integer nonconvex optimization problem. To efficiently solve it, we decompose the optimization into two stages. The first stage is to minimize time consumption in an SN-to-UAV nonorthogonal multiple access process and we solve it by designing a UAV deployment scheme, a subchannel matching scheme, and a joint power and time allocation scheme, based on which, the second stage is to maximize the residual energies of USNs in USN-to-SN transmissions under a modified frequency-division multiple access strategy in each collection cycle. The second-stage optimization is further decomposed into some similar subproblems, and each of them is considered as a bipartite graph matching problem between USNs and underwater acoustic channels. For each subproblem, we propose improved weight-based matching and bisection-based searching algorithms. Finally, we design a low-complexity iteration algorithm to approximate the optimal solution of the original problem by solving these subproblems. The simulation results validate the effectiveness of our proposals.

In this paper, we consider a single cell downlink non-orthogonal multiple access (NOMA) network and aim at maximizing the energy efficiency. The energy-efficient resource allocation problem is formulated as a non-convex and NP-hard problem. To decrease the computation complexity, we decouple the optimization problem as a subchannel matching scheme and power allocation subproblems. In the subchannel matching scheme, a non-cooperative game is applied to model this problem. To discuss the existence of Nash equilibrium (NE), we introduce a super-modular game and then design an algorithm to converge to the NE point. Moreover, a greed subchannel matching algorithm with low complexity is given through a two-way choice between users and subchannels. However, for given subchannel matching scheme, power allocation is still a non-convex problem, which is difficult to get the optimal solution. We then transform the non-convex problem to a convex problem by applying a successive convex approximation method. Afterward, we provide an algorithm to converge to suboptimal solution by solving a convex problem iteratively. Finally, simulation result demonstrates that the energy efficiency performance of the NOMA system is better than the orthogonal frequency division multiple access system.