Research On Spectrum Resource Allocation Strategy To Guarantee QoS In Cellular Network Of Vehicles

With the rapid development of communication technology and the continuous advancement of 5G commercialization,the application of cellular communication technology in intelligent transportation systems is gradually becoming mature.The C-V2 X technology(Cellular-vehicle to evertything)based on cellular communication technology aims to improve the information interaction capability of vehicles.Under the real conditions of limited spectrum resources,through the information interaction with the surrounding environment,vehicles,infrastructure and other entities,the vehicle’s environmental perception capability can be expanded in time and space,thereby improving traffic efficiency and ensuring road safety.C-V2 X allows direct communication between terminal devices based on D2D(device to device),supports the distributed resource scheduling mode of self-organization of terminal devices outside the base station range and the centralized resource scheduling mode assisted by the base station within the base station range,which provides a lower delay for V2 X communication and can meet the Qo S requirements of the diversified services of the intelligent transportation system.C-V2 X technology allows vehicle users to reuse cellular spectrum resources.Under different network load conditions,centralized and distributed spectrum resource scheduling schemes directly affect user experience.Appropriate spectrum algorithms can significantly improve system performance.However,the interference problem introduced by spectrum reuse,the change of cellular network topology caused by vehicle movement,and the continuous change of channel state information lead to more challenges in the allocation of spectrum resources in cellular vehicle-mounted networks.This paper focuses on the cellular vehicle-mounted mobile network in the urban traffic environment.In view of the shortage of spectrum resources,different Qo S requirements for various services,and the difficulty of ensuring security services during peak traffic,starting from the actual network load scenarios and service requirements,we deeply study the spectrum resource allocation strategies in cellular-based vehicular networks under different load network conditions.The main research work of the paper is as follows:(1)Research on resource scheduling algorithms for throughput improvement in general network load scenarios: V2 X communication scenarios based on PC5 and UU interfaces are studied,and the channel model based on slow-changing large-scale fading and fuzzy processing of small-scale fading in a single scheduling period is constructed.The Qo S requirements of infotainment services and safety and efficiency services are analyzed,and a low-complexity communication resource allocation algorithm that maximizes system throughput is designed on the basis of satisfying user service Qo S requirements.(2)Research on resource scheduling algorithms for ensuring driving safety in high-load network scenarios: On the basis of general network load scenarios,this paper further studies the lack of spectrum resources caused by concurrent access of multiple services during peak traffic periods,and it is difficult to satisfy security services.Taking full advantage of the fact that non-secure services are not sensitive to delay compared with secure services,we use the base station to assist in controlling the service delivery order on a single spectrum resource.A low-complexity spectrum resource allocation algorithm is designed to ensure the performance of security services.(3)Facing the uncertain environment,a joint optimization algorithm for adaptive spectrum resource allocation scheme selection is designed based on real-time network load conditions,and the allocation of licensed and unlicensed spectrum resources is carried out from the global level.Through the NS-3 simulation software,we built a cellular vehicle communication network prototype system under the coverage of a single base station,and verified the effectiveness of the algorithm under different network load conditions.