Emerging intelligent transportation services are latency-sensitive with heavy demand for computing resources, which can be supported by a multi-tier computing system composed of vehicular edge computing (VEC) servers along the roads and micro servers on vehicles. In this work, we investigate the dual Uu/PC5 interface offloading and resource allocation strategy in Cellular Vehicle-to-Everything (C-V2X) enabled multi-tier VEC system. The successful transmission probability is characterized to obtain the normalized transmission rate of PC5 interface. We aim to minimize the system latency of task processing while satisfying the resource requirements of Uu and PC5 interfaces. Due to the non-convex and variables coupling, we decompose the original problem into two subproblems, i.e., resource allocation and offloading strategy subproblems. Specifically, we derive the closed-form expressions of packet transmit frequency of PC5 interface, transmission power of Uu interface, and CPU computation frequency in the resource allocation subproblem. Moreover, for the offloading strategy subproblem, the offloading ratio matrix is obtained by proposing the PC5 interface based greedy offloading (PC5-GO) algorithm, which concludes offloading decision and ratio. Simulation results are provided that the proposed PC5-GO algorithm can significantly improve the system performance compared with other baseline schemes by 13.7% at least.

Many emerging intelligent transportation services are latency-sensitive with heavy demand for computing resources, which can be handled by a multi-tier computing system composed of vehicular edge computing (VEC) servers in the roadside and micro servers carried by vehicles. In multi-tier VEC system, the offloading of vehicle-to-vehicle (V2V) can be supported using the Cellular Vehicle-to-Everything (C- V2X) links, through Uu or PC5 interfaces. In this work, we investigate the offloading and resource strategy in C- V2X enabled multi-tier VEC system. The successful transmission probability of PC5 interface is modeled to characterize the normalized transmission rate of C- V2X link. We aim to minimize the total system latency of the task processing to optimize the offloading ratio matrix and packet transmit frequency of the PC5 interface, and computation resource allocation of vehicles and VEC server. Due to the non-convex and variables coupling, the latency minimization problem is decomposed into two subproblems, i.e., resource allocation and offloading strategy subproblems, and propose a PC5 interface based greedy offloading (PC5-GO) algorithm. Specifically, for the resource allocation subproblem, we derive the closed expressions of packet transmit frequency of PC5 interface and CPU computation frequency at vehicle and VEC server. For the offloading strategy subproblem, the offloading ratio matrix is obtained by the proposed PC5-GO algorithm. Simulation results are provided that the proposed PC5-GO algorithm can significantly enhance the system performance compared with other benchmark schemes by 5.88% at least.

As one of the essential application scenarios of autonomous driving, vehicle platoon has remarkable advantages in enhancing traffic capacity and reducing fuel consumption. When considering cellular vehicle-to-everything (C-V2X) supported platoon communication in autonomous mode, the existing theoretical analysis works for C-V2X can not be applied to the vehicle platoon scenario due to the unique movement features of the platoon led by the platoon leader. In this paper, an analytical model is proposed to analyze the packet delivery probability of the platoon in C-V2X autonomous mode, using stochastic geometry. Theoretical analysis and simulation verification of the proposed model is carried out and it is demonstrated that the analysis results are in accordance with the simulations results. The proposed analytical model is of significance to evaluate the performance and provide design insights for the platoon communication supported by C-V2X.

In this work, we investigate the transmission and offloading strategy in the nonorthogonal multiple access (NOMA)-enabled fog computing system for the Internet of Things (IoT). We aim to minimize the total energy consumption of the IoT system while satisfying the latency requirements. Due to the energy minimization problem is a mixed-integer nonlinear programming, we decompose the problem into two subproblems for different optimizing variables, i.e., fog node selection and resource allocation subproblems, and propose a multinode collaboration transmission and computation (MCTC) algorithm. Specifically, the fog node selection subproblem can be transformed into the assignment problem, which is constructed as a bipartite graph to obtain the node selection strategy. For the resource allocation subproblem, we propose an iterative algorithm to obtain the offloading workload, duration allocation, and computation resource. Simulation results are provided, which demonstrate that the proposed algorithm outperforms the other strategies by 56.88% at least.

Fifth-Generation mobile networks for Railway (5G-R) is a promising train-ground communication solution to deal with the design challenges on ubiquitous connections and high reliability transmission for smart railways. Considering the linear coverage scenario along the railway lines, deep overlapping coverage can enhance the coverage reliability, but the capacity of cell edge users is plagued by severe inter-cell interference. In this paper, the fundamental performance of 5G-R systems with linear redundant coverage is investigated. We quantitatively analyze the impact of inter-cell interference on the capacity of cell edge users, in which the exponential effective signal to interference plus noise (SINR) mapping method is utilized to analyze SINR distribution of 5G-R users. Then, we investigate the coverage reliability considering the base station (BS) failure. Finally, taking cell edge user capacity as the optimization objective, we analyze the cell edge user capacity-coverage reliability tradeoff for 5G-R systems, and then provide useful insights into BS deployment for 5G-R systems through performance simulations and numerical results.

The safety of autonomous driving can be improved with the support of Cooperative Vehicle-Infrastructure System (CVIS) and Vehicular Edge Computing (VEC), which benefit greatly from crowdsensing of CVIS and accurate decision in a short deadline of VEC. In the CVIS, the vehicles will upload the crowdsensing data to the VEC server for data fusion and tasks generating. However, with the ever-increasing number of vehicles, the VEC server cannot undertake massive computation-intensive tasks due to the limited edge computing capabilities. In this paper, we propose a reverse offloading framework to fully utilize the computation resource of vehicles to relieve the burden of the VEC server in a multi-vehicle mobile edge network. First, the system latency minimization problem is formulated as a mixed integer nonlinear programming problem by optimizing reverse offloading decisions and the communication and computation resources allocation. Next, the original problem is transformed into an equivalent weighted-sum optimization problem, which can be decoupled as two subproblems, i.e., resource allocation and decision selection subproblems. The closed-form expressions for the optimal resource allocation are derived by the dual decomposition method in a distributed fashion. Moreover, a low complexity greedy based efficient searching (GES) algorithm is proposed to obtain the reverse offloading decision strategies. Simulation results show that the proposed algorithm can significantly improve the performance compared with other baseline schemes.

Vehicular Edge Computing (VEC) is becoming popular due to the high offloading demand of computation-intensive vehicular network applications. Although the mobility of vehicles poses great challenges to the computation offloading as the fast time-varying fading channel, the moving vehicles can be served as mobile relays to achieve opportunistic transmission for improving offloading efficiency. In this paper, a scalable modulation (S-Mod) based task offloading (SMO) strategy is proposed, in which the vehicles can be selected as mobile relay to implement cooperative transmission based on S-Mod scheme. To reduce the energy consumption of vehicles, the energy consumption minimization offloading decision problem with delay and transmission quality requirement constraints is formulated. A hybrid offloading algorithm with S-Mod scheme design and relay selection is proposed. Our simulation results show that the proposed SMO strategy can outperform the existing strategy up to by 72.68% on energy consumption and 14.15% on execution time.