Research On Efficient Wireless Resource Management For Internet Of Vehicles

The Internet of vehicles(IoV)is an important part of future intelligent transportation,it is an open and integrated network system composed of multiple users,multiple vehicles,multiple objects,and multiple networks,which can realize the deep integration of human-vehicle-object-environment,thereby improving traffic efficiency and enhancing urban service levels.However,with the emergence of a large number of compute-intensive emerging applications and the diversified demands of in-vehicle services types pose higher challenges to the reliability,efficiency and real-time capabilities of IoV communication.In the case of limited resources,resource allocation method is an important factor affecting system performance,and how to make reasonable and efficient resource allocation is always one of the key issues to be solved in the field of IoV.Therefore,in order to address the above challenges,this work will take optimization of resource management methods for IoV and the expansion of computing resources as the starting point,and mainly use indicators such as energy efficiency,system utility,data rate and total computing offload delay as the criteria for improving system performance.By combining convex optimization theory,edge computing and other technologies,a multi-dimensional resource management scheme is designed to meet diverse needs for different IoV scenarios,thereby providing better quality of service(QoS)for in-vehicle users.The main work and innovation are summarized as follows:1.A resource management mechanism for subchannel matching and power allocation to maximize the energy efficiency of autonomous driving vehicular network is proposed,which alleviates the problem of energy efficiency decline caused by complex traffic environment and effectively improves the QoS of vehicles.Specifically,first of all,this work designs a network architecture including high-speed intersections and cross intersections,which effectively guarantees network communication with different communication technologies.Based on this architecture,the work also constructs a network model of the autonomous driving vehicular network based on non-orthogonal multiple access,considering constraints such as cross-layer interference and QoS,so an expression of the energy efficiency is given.Secondly,in order to maximize the energy efficiency of system,a subchannel matching algorithm is proposed,which can ensure the gain of the vehicle is maximized on its chosen subchannel.After the subchannel allocation matrix is obtained,the power allocation problem is futher simplified,and then the alternating direction multiplier algorithm is introduced to obtain the optimized power allocation matrix.Finally,the simulation results show that the proposed algorithm can achieve higher energy efficiency compared with other algorithms,which further improves the network performance.2.A resource management method for subchannel and power optimization of fog computing vehicular network energy efficiency optimization is proposed,which effectively solves the problem of network congestion caused by too many local in-vehicle tasks in IoV.Firstly,this work establishes a system model for fog computing vehicular network,in which devices at the edge of the network are able to take on part of the computation and storage tasks as a way to reduce network latency.Secondly,an optimization model with the goal of maximizing energy efficiency considering the caching case is constructed,and an emerging intelligent optimization algorithm(chemical reaction optimization algorithm)is used to solve the objective function.To facilitate the solution,the optimization model with constraints is transformed into an unconstrained optimization model by introducing a penalty function,which further enables to obtain the expression of molecular potential energy in the chemical reaction optimization scheme.Then the subchannel allocation matrix and power allocation matrix are optimized based on the chemical reaction optimization algorithm and the real-coded chemical reaction optimization algorithm,respectively,and the optimal solution is obtained by finding the minimum potential energy.Finally,the simulation results show that the proposed algorithm can reduce the redundant communication overhead.Moreover,the algorithm with caching can improve the resource utilization compared to the algorithm without caching.3.A resource management method for maximizing sum rate in the space-airground integrated vehicle network(SAGVN)is proposed,which effectively solves the problems of difficult resource scheduling and excessive end-to-end delay in the SAGVN.Specifically,this work designs an edge-enhanced SAGVN architecture.Based on this architecture,the resource allocation in scenarios with or without user association is considered separately.In the scenario without user association,firstly,the objective function of maximizing sum rate of small cells in SAGVN considering subchannel allocation and power allocation is derived.Secondly,a subchannel allocation algorithm is designed to ensure the maximum signal-to-interferenceplus-noise ratio(SINR)of vehicles on the subchannel.On this basis,the Lagrangian dual method is further used to solve the power allocation problem,and a powerbased non-cooperative game model is given,and the existence of its Nash equilibrium is proved.Similarly,for the case of considering user association,the objective function of maximizing sum rate of small cell considering user association,subchannel allocation and power allocation is derived,and its subchannel algorithm and power allocation algorithm are consistent with those in the scenario without considering user association,and a low-complexity user association algorithm is proposed to optimize the connection between vehicles and base stations,and then the subchannel and power optimization are performed.Thus,the maximization of the sum rate can be achieved.Finally,simulation results show that the proposed algorithm can ensure reliable communication of the vehicles and effectively mitigate the negative impact due to excessive time delay.