Research On Resource Allocation Algorithms For C-V2X Vehicular Network

With the fast development of internet of things and intelligent transportation system,the cellular vehicle-to-everything(C-V2X)communication,relying on the development of 5G communication,not only has the advantages of wide coverage and high rate,but also has the potential to meet the upcoming strict demands of high reliability and low latency,such as automatic driving.When massive vehicles communicate with each other in dense urban areas,it's important to design efficient resource allocation and interference management algorithms to improve energy efficiency,spectrum efficiency and system throughput while guaranteeing the low latency and high reliability.This thesis focuses on the resource allocation of vehicular network based on the C-V2 X technology.The main research contents and innovations of this thesis are summarized as follows:1.In the non-orthogonal multiple access(NOMA)based cellular network with vehicle-to-vehicle(V2V)communication,aiming at the co-channel interference between V2 V users and cellular users as well as the power allocation problem based on the NOMA principle,we propose an energy efficiency dynamic resource allocation algorithm.Firstly,a stochastic optimization model is established to maximize the energy efficiency by considering subchannel scheduling,power allocation and congestion control,in order to guarantee the delay and reliability of V2 V users while satisfying the rate of cellular users.Then,leveraging on the Lyapunov stochastic optimization method,the traffic queues can be stabilized by admitting as much traffic data as possible to avoid network congestion,and the radio resource can be allocated dynamically according to the real-time network traffic and thus a suboptimal subchannel matching algorithm is designed to obtain the user scheduling scheme.Furthermore,the power allocation policy is obtained by utilizing successive convex optimization theory and Lagrange dual decomposition method.Finally,the simulation results show that the proposed algorithm can improve the system energy efficiency and ensure the quality of service(QoS)requirements of different users and network stability.2.To solve the problems that cloud computing is difficult to meet the delay-sensitive and low power consumption service requirements in the vehicular network and the network congestion of massive devices caused by computation offloading,a joint computation offloading and resource allocation algorithm based on cloud-fog hybridnetwork architecture is proposed.Firstly,considering the collaboration of fog and cloud layers for computation offloading,a resource optimization model is established to minimize the utility functions by taking the maximum delay as the constraint.Then,a low-complexity joint offloading decision and computationa resource allocation algorithm is proposed by applying the semi-definite relaxation and binary search.Furthermore,the overflow probability estimation model of offloading users' request queues is established,and an online measurement-based time-frequency resource allocation algorithm for fog nodes is proposed.Finally,with the help of fractional programming theory and the Lagrange dual decomposition method,the bandwidth and power allocation strategy of users associated to the fog node is obtained.The simulation results show that the proposed algorithm can minimize the system utility function while satisfying the latency requirement.