| As an important manifestation of the Internet of Things in the traffic system,Vehicle to Everything(V2X)covers Vehicle to Vehicle(V2V),Vehicle to Pedestrian(V2P),Vehicle to Infrastructure(V2I)and other types of communication.Within these types of communication,V2 V and V2 I are the key technologies to solve the traffic safety problems.Therefore,this paper focuses on the issue of wireless resource allocation in the Internet of Vehicles for V2 V and V2 I communication.The main work is as follows:Firstly,this paper summarizes the current research status of the Internet of Vehicles and Non-Orthogonal Multiple Access(NOMA).Then,the resource scheduling schemes under the current two standards of Internet of Vehicles are introduced,and the resource scheduling of MAC layer is explored respectively.The simulation results show that the Carrier Sense Multiple Access with Collision Avoidance(CSMA/CA)protocol cannot deal with the problem of multiple terminals competing for the same resource effectively.The Sensing-based Semi-Persistent Scheduling(SBSPS)can deal with the resource competition by listening to the past resource usage,but this scheme has the defect of low spectrum utilization,so it can be concluded that: current standard resource scheduling scheme cannot show comprehensive advantages in terms of quality of service,spectrum efficiency and system throughput.Then,a basic downlink NOMA system model is introduced,and an important corollary is derived: the system throughput of downlink NOMA system is proportional to the power allocation factor.Due to the existing standard MAC layer resource allocation scheme can not meet the needs of efficiently handling resource competition and high spectrum efficiency,to improve the spectrum efficiency and system throughput of the Internet of Vehicles system,this paper studies the wireless resource allocation problem under the coexistence of V2 V and V2 I,which involves two sub-problems: power control and channel resource allocation.In the aspect of power control,through theoretical derivation of ergodic capacity and outage probability under constraint conditions,three important inferences are summarized.A power control algorithm based on genetic algorithm is proposed to maximize the system throughput based on proportional fairness factor.In the aspect of channel allocation,a channel allocation algorithm based on Gail Shapley’s stable matching idea is proposed.Simulation results show that the proposed algorithm has excellent performance both in terms of algorithm complexity and performance.Finally,in order to further improve the spectrum efficiency and solve the problem of vehicles simultaneously communication with multiple vehicles in dense areas,this section introduces 5G potential technology—NOMA into the Internet of Vehicles system,and continues to conduct research on resource allocation.To improve the transmission rate of V2 V communication,it is assumed that a V2 V can reuse the channel resources of multiple V2 I cellular users.In dealing with the power control problem,the idea of optimal variable rotation is adopted in this paper.Firstly,the transmission power is assumed to be constant to solve the power control problem in the V2 V vehicle group.According to the above inferences,we prove the concavity and convexity of the current problem,and solve the optimal power distribution factor by Lagrange multiplier method and KKT condition.Then,based on the power distribution factor solved in the previous step,the problem of finding the optimal transmission power was reduced to a convex optimization problem with maximized lower bounds by using Dinkel Bach and the Concave Procedure(CCCP)algorithm.An algorithm based on iteration is proposed to maximize the energy efficiency of the system on a single subchannel.For spectrum allocation,the channel allocation problem is modeled as a many-to-one matching problem,and a stable many-to-one matching algorithm is proposed.The simulation results show that compared with the comparison algorithm,the proposed algorithm can greatly improve the system energy efficiency. |
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