To enhance target sensing capabilities in low Earth orbit (LEO) satellite communication systems, this paper proposes a LEO satellite-enabled integrated sensing and communication (ISAC) beamforming (LSEIB) algorithm. Specifically, a LEO satellite-enabled bistatic ISAC system is constructed, in which the LEO satellite simultaneously transmits both communication and sensing signals, while the ground gateway receives the echoes to enable cooperative sensing. To balance communication performance and sensing accuracy, the upper bound of the achievable communication rate based on statistical channel state information and the Cramér-Rao bound (CRB) expression for two-dimensional angle-of-arrival estimation are derived. A joint beamforming optimization problem is then formulated with the objective of minimizing the CRB. Subsequently, a semidefinite relaxation method is introduced to transform the original non-convex problem into an equivalent semidefinite program, and a closed-form rank-one solution is constructed to satisfy the rank constraint. Simulation results demonstrate that the proposed LSEIB algorithm exhibits great convergence and optimization performance under various settings, and the proposed bistatic architecture achieves significant improvements in sensing performance compared to the monostatic counterpart.

The rapid development of space-air–ground integrated technology has laid a foundation for achieving wide area wireless communication coverage, but its applicability is limited due to the limited computational capacity and energy storage of the terminal devices. This article thus proposes a space-air–ground integrated mobile-edge computing (MEC) task offloading and computing resource allocation (SIMOC) algorithm. First, a space-air–ground integrated MEC system is designed in which the terminal devices in the system can offload tasks to air- and space-based servers while performing energy harvesting. Second, an optimization objective of maximizing the task execution benefit minus the sum cost of task offloading and execution is established, for which the problem is transformed into a time-slot-based minimization problem of queue-stability minus revenue using Lyapunov optimization. Finally, the energy harvesting and task offloading subproblems of the queue-stability-minus-revenue problem are solved, respectively, to maximize the total revenue while ensuring device stability. Simulation results show that the SIMOC algorithm can reduce the all-task completion time by up to 98.16% compared with only local task execution algorithm in the absence of newly added tasks and shows good performance in handling newly added tasks. Meanwhile, the SIMOC algorithm has better performance compared to the particle swarm optimization algorithm.

Abstract To solve the problems of unbalanced network loads, poor network throughput, and low user transmission rate in 5th generation (5G) vehicular networks, a user‐centric data communication service (UCDCS) strategy is proposed for 5G vehicular networks. First, the UCDCS model is established. The access roadside unit group (ARSUG) is updated in real‐time according to predictions of vehicle mobility. Then, a data communication service resource allocation algorithm based on game theory that considers the network load cost, throughput cost, and vehicle benefit is developed. Based on the results of the algorithm, roadside unit (RSU) selection based on entropy weight is performed and the best RSU for data communication is selected according to the different service preferences of each vehicle. This improves the transmission rate for users and realizes network load balance to ensure the quality of service for users. The simulation results show that the transmission rate of the UCDCS strategy is 35.16%, 23.46%, and 47.74% higher than those of the traditional handover management (THOM), improved mobility management (IMM), and network‐centric network selection strategies (NCNS), respectively; correspondingly, the link reliability is at least 0.07% higher, and the network delay is at least 5.96% smaller for the UCDCS strategy.

The Internet of vehicles currently faces the challenges of spectrum interference in multimode, multiservice communications, and low efficiency in switched-vehicle selection for vehicular cooperative communication. To overcome these, a user-centered cooperative-communication strategy (UCCS) is proposed. A system model of the UCCS algorithm is established for three situations according to the vehicular cooperative-communication process. When there are service roadside units (SRSUs), cooperative RSUs (CRSUs), and cooperative vehicles (CVs) in the service vehicle (SV) communication range, a two-layer game method is proposed, which dynamically adjusts the transmission power, punishment factor, and other parameters of service and cooperative vehicle. When there are only CVs in the SV communication range, a multiparameter voting method is proposed, which selects the best cooperative-communication vehicle by calculating the normalized linear weighted value of CVs. When there are only CRSUs and CVs in the SV communication range, a station-vehicle voting method is proposed, which calculates the cooperative-communication value of CRSUs to select the optimal one, thus comparing it with the best CV to determine the optimal cooperative-communication object. The simulation results show that the average throughput of the UCCS algorithm is increased by 17% and 42% as compared to those based on the repeated game and network utility maximization algorithms, respectively, and its average waiting time decreased by 43% and 24% compared to those of the random selection and shortest path selection algorithms, respectively. The overall system revenue and communication performance of the proposed algorithm are significantly improved.

Abstract Due to the roadside unit can not fully achieve seamless coverage and the dynamic change of network topology in vehicular networks, problems such as the interruption of vehicle service, poor stability of communication link, and high network delay will be caused. To solve these problems, this paper proposes a codirectional multilane adaptive message return (CMAMR) strategy based on edge computing. Firstly, we establish a CMAMR communication model. Then, according to the movement trajectory of the receiving node, we propose a single-hop and a multi-hop message return link strategy. The former combines the single-hop link quality and node location information to construct a node selection function to assist the head node in selecting an optimal forwarding node to form a single-hop message return link. The latter assists the RSU to select the optimal multi-hop message return link in the same lane by establishing a link adaptive quality evaluation function, thus improving the user’s quality of server. The simulation results show that the link network delay of the CMAMR strategy is 14.6%, 42.86%, and 38.47% lower than those of the random with forwarding progress (RFP), most forward with fixed radius (MFR), and traditional vehicle-to-infrastructure and vehicle-to-vehicle cooperative communication (TVCC) message return strategies, respectively; correspondingly, the link reliability is at least 9.13% higher for the CMAMR strategy.

As a prevailing concept in 5G, virtualisation provides efficient coordination among multiple radio access technologies (RATs) and enables multiple service providers to share different RATs’ physical infrastructures. This study proposes a generic framework for virtualising heterogeneous networks with different RATs. A novel virtual medium access control concept is introduced to realise resource offloading (allocation) and mobility management of the framework. The offloading and handover protocols are designed in detail. A novel resource offloading strategy is devised: First, to model the fact that different RATs possess different adaptability to different services, an ‘adaptability ratio’ concept is introduced and calculated using grey relational analysis. After that, a matching theory based framework is proposed to formulate the resource offloading problem and a ‘deferred acceptance’ algorithm is introduced to solve it. Through simulation, it is proved that the proposed protocols can efficiently reduce the service interruptions and improve the resource usage.