Mobile edge computing (MEC) enhanced satellite based internet of things (SAT-IoT) is an important complement for terrestrial networks based IoT, especially for the remote and depopulated areas. For MEC enhanced SAT-IoT networks with multiple satellites and multiple satellite gateways, the coupled user association, offloading decision, computing and communication resource allocation should be jointly optimized to minimize the latency and energy cost. In this paper, the latency and energy optimization for MEC enhanced SAT-IoT networks are formulated as a dynamic mixed-integer programming problem, which is hard to obtain the optimal solutions. To tackle this problem, we decompose the complex problem into two sub-problems. The first one is computing and communication resource allocation with fixed user association and offloading decision, and the second one is joint user association and offloading with optimal resource allocation. For the sub-problem of resource allocation, the optimal solution is proven to be obtained based on Lagrange multiplier method. And then, the second sub-problem is further formulated as a Markov decision process (MDP), and a joint user association and offloading decision with optimal resource allocation (JUAOD-ORA) is proposed based on deep reinforcement learning (DRL). Simulation results show that the proposed approach can achieve better long-term reward in terms of latency and energy cost.

Satellite assisted vehicle-to-vehicle (V2V) communication can provide services for vehicles in depopulated areas, and it can be employed as an effective complementary component for terrestrial vehicular networks. Since the available communication and computing resource for satellites are scarce, task offloading, computing and communication resource allocation, which are coupled with each other, are critical issues for satellite assisted V2V communication. To tackle these problems, we formulate the joint offloading decision, computing and communication resource allocation problem for satellite assisted V2V communication as a mixed-integer nonlinear programming problem with minimum weighted-sum end-to-end latency, and we decouple it into two subproblems. First, the Lagrange multiplier method is adopted to obtain the optimal computing and communication resource allocation with fixed offloading decision. Then, the results of the resource allocation subproblem are fed into the offloading decision problem, which is formulated as a Markov decision process. To maximize the long-term reward of offloading decision, a deep reinforcement learning based method is adopted to learn the optimal offloading decision. Finally, the simulation results show that the proposed joint task offloading and resource allocation approach has superior performance compared with other schemes.

Benefiting from the development of satellite on-board processing capability, the orbital computing can be realized by deploying edge computing servers on satellites to reduce the task processing latency. However, edge computing based on geostationary Earth orbit (GEO) or low-Earth orbit (LEO) alone can hardly meet the latency requirements of Satellite-assisted Internet of Things (SIoT) services. Moreover, the uneven distribution of tasks generated by SIoT devices will also cause the load unbalancing among different satellites. In this article, hybrid GEO–LEO SIoT networks is investigated with joint computing and communication resource allocation. To tackle the load unbalancing problem, tasks generated by SIoT devices can be processed by collaborative LEO satellites or forwarded to gateways on ground via GEO satellite. Thus, the joint task offloading, communication and computing resources allocation for the hybrid SIoT network can be formulated as a mixed integer dynamic programming problem with satellites-ground cooperation and intersatellite cooperation via the intersatellite links. Then, an intelligent task offloading and multidimensional resources allocation algorithm (TOMRA) is proposed to minimize the latency of task offloading and processing. First, a method base on deep reinforcement learning is utilized to solve the subproblem of task offloading and channel allocation. And then, convex optimization is adopted to solve the subproblem of computing resource allocation under fixed offloading and channel allocation decisions. Simulation results show that the proposed TOMRA can achieve better performance than the reference schemes.

With the rapid development of low earth orbit (LEO) constellation satellite networks and on-board processing (OBP), in-network content caching becomes feasible which can further improve system performance. In order to minimize user terminals content access delay, a novel caching algorithm by optimizing content placement in LEO satellite constellation networks is proposed in this letter. The formulated problem is proved to be NP-Complete (NPC) by reducing it to the 2-Disjoint Set Cover (2DSC) problem which is a known NPC problem. This paper considers the interactions among distributed satellites for making individual content decisions. Then, this paper adopts an exchange-stable matching (ESM) algorithm based on many-to-many matching game with externalities. Simulation results show ESM algorithm can improve the system performance in terms of content access delay.

Multibeam satellite communication plays an important role in future integrated satellite-terrestrial communication systems. However, the large area covered by the satellite beams makes itself vulnerable to be wiretapped, especially for the cases that eavesdroppers are located near the legitimate receivers. In this paper, cooperative jamming from both satellite and full-duplex receivers are investigated where we aim to minimize the power consumed by a multibeam satellite system with secrecy outage probability constraints. To tackle this problem, an algorithm of joint beamforming and power allocation via alternating optimization (JBFPA-AO) is proposed. Specifically, a feasible solution is first obtained by transforming the original non-convex problem into a semi-definitive programming (SDP) problem with relaxed constraints. And then, alternating optimization is adopted to find better solutions iteratively. Convergence of the proposed JBFPA-AO is proved, and simulation results show its effectiveness to reduce the power utilized by the satellite.