Study on Task Offloading Algorithm for Internet of Vehicles on Highway Based on 5G MillimeterWave Communication

With the rapid development of the Internet of vehicles,the emerging new types of in-vehicle tasks put forward higher requirements for communication and computing capabilities.The development of satellite communication technology and the large-scale deployment of 5G millimeter-wave base stations provide safer and more reliable services for highway vehicle users.At the same time,mobile edge computing technology deploys mobile edge computing(MEC) servers with computing and storage capabi-lities around user terminals to provide computing services for on-board tasks while reducing transmission delays.Aiming at the problem of offloading decision-making and communication resource allocation of vehicle tasks in highway scenarios,the joint optimization problem of computing and communication resources is modeled as a 0-1 mixed integer linear programming problem.Firstly,the original optimization problem is decoupled into the resource block allocation sub-problem and the offloading decision sub-problem.Secondly,the sub-problems are solved by using the water injection algorithm and the particle swarm algorithm.Finally,the sub-problems are iteratively solved based on the heuristic algorithm to obtain the optimal resource block allocation scheme and offload decision vector.Simulation results show that the algorithm minimizes the average system delay while meeting the requirements of all on-board missions

Optimal association of mobile users to multi-access edge computing resources

Multi-access edge computing (MEC) plays a key role in fifth-generation (5G) networks in bringing cloud functionalities at the edge of the radio access network, in close proximity to mobile users. In this paper we focus on mobile-edge computation offloading, a way to transfer heavy demanding, and latency-critical applications from mobile handsets to close-located MEC servers, in order to reduce latency and/or energy consumption. Our goal is to provide an optimal strategy to associate mobile users to access points (AP) and MEC hosts, while contextually optimizing the allocation of radio and computational resources to each user, with the objective of minimizing the overall user transmit power under latency constraints incorporating both communication and computation times. The overall problem is a mixed-binary problem. To overcome its inherent computational complexity, we propose two alternative strategies: i) a method based on successive convex approximation (SCA) techniques, proven to converge to local optimal solutions; ii) an approach hinging on matching theory, based on formulating the assignment problem as a matching game