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.

Information dissemination in mobile networks turns out to be a problem when the network is sparse. Mobile networks begin to establish a separate cluster attributable to the limited communication range of terminals. The multibeam satellite communication systems can play a significant role in providing direct-to-user satellite mobile services and connecting the separated clusters. This paper focuses on how to efficiently schedule limited satellite-based radio resources to enhance transmission efficiency and meet the requested traffic with low complexity. Taking the inter-beam interference and resource utilization variance into consideration, we build a game theoretic based model for bandwidth allocation in the forward link. As the size of satellite beams increases, the size of the action space for deep reinforcement learning based on a single agent becomes large, resulting in high time complexity. Thus, we extend the single-agent deep reinforcement learning to the multi-agent context and then propose a cooperative multi-agent deep reinforcement learning method to achieve the optimal bandwidth allocation strategy. Each beam works as a player who is willing to satisfy the request traffic with flexible payloads. We built a multi-beam satellite platform using real historical data. The experimental results show that this approach is capable of enhancing transmission efficiency and can be flexible to achieve the desired goal with low complexity.

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.

The rapid development of smart healthcare and the increasing number of wearable devices in consumer electronics can facilitate health monitoring services with lower latency and less energy consumption, and provide more reliable computation and communication for medical cares. In this paper, we consider the next generation (next-gen) wearable consumer electronics architecture and introduce D2D communication in a trustworthy health monitoring system that utilizes collaborative computing among different wearables to reduce the execution cost of health monitoring tasks and improve the efficiency of medical services, which can monitor several health indicators of the human body in real-time. We further construct a device-collaborative offloading framework for health monitoring (DCO-HM) to reduce energy consumption and computational latency. A directed acyclic graph (DAG)-based offloading algorithm, in which wearables can execute medical tasks locally or perform device-collaborative computation, is proposed. We also consider the priority of task execution and the CPU frequency of wearables to achieve distributed smart monitoring. The simulation results show that the proposed device-collaborative computing scheme significantly improves the computational efficiency of smart health monitoring system with a trustworthy manner.

The vertical industry member and the Internet of Things (IoT) business are spurring heated discussions on the evolution of future mobile communication. The emerging non-terrestrial network (NTN) plays increasingly important role in addressing service and coverage demand boom, whilst they still envisage technical and performance bottlenecks. Besides, traditional communication paradigm is constrained by Shannon theorem and fails to exploit the semantic features. In this article, we review the classic 6G NTN IoT networks and semantic communication in terms of structure, challenges, and applications. Then we observe that the superiority of semantic communication in low signal-to-noise ratio (SNR) regime is complementary to 6G NTN. We further propose an adaptive semantic-empowered 6G NTN IoT structure supporting communication paradigm switching. Finally, the open issues are discussed.

The metaverse employs globally distributed computing and communication infrastructures to construct an immersive digital world. Its continuous synchronization and hyperinteractivity create a dilemma involving tremendous volumes of sensory data and scarce spectrum resources. Connected and automated vehicle (CAV) networks integrate onboard sensing, communication, computation, and storage capabilities to enhance the metaverse. This article introduces an edge intelligence-based mobile crowdsensing (MCS) CAV framework, which studies both perception and transmission dimensions. The metaverse’s cornerstone is high-quality edge sensory data delivery across a geographical distribution. Thus, we construct a CAV crowdsensing-based traffic coverage model. Furthermore, we provide information silos in urban transportation networks as a use case. Simulation results validate the proposed framework’s superiority in improving MCS coverage and perceptual data offloading efficiency. With edge intelligence, such a framework can illuminate the prospect of the convergence between CAV applications and the metaverse.