Research On Energy-efficient Resource Allocation In Vehicular Heterogeneous Networks

With the evolutionary development of urbanization and automobile industry,modern transportation systems with billions of vehicles cause a series of critical problems such as increased energy consumption,air pollution,and greenhouse gas emission.Among various fundamental technologies,vehicle-to-vehicle(V2V)communication,which supports ubiquitous information exchange and content sharing among vehicles with little or no human intervention,is a key enabler for realizing the ITS framework.In the case of more vehicles and less channel resources,the traditional V2V communication technology has a certain transmission delay,and the device-to-device(D2D)-based vehicle communication method(D2D-V2V)can be effective.To solve the problem of spectrum resource allocation and content distribution,it is necessary to design an effective spectrum resource management solution to maximize the energy efficiency of vehicular heterogeneous networks.In this paper,we investigate how to realize energy-efficient vehicular heterogeneous networks for green cities by exploring cooperative two-hop device-to-device based vehicle-to-vehicle(D2D-V2V)transmission.We propose a two-stage energy-efficient resource allocation algorithm.In the first stage,an auction-matching based joint relay selection,spectrum allocation and power control algorithm is derived,which employs an English-auction approach for matching preference updating and conflict avoidance,and optimizes the energy efficiency of two-hop D2D-V2V and cellular links simultaneously in an iterative fashion.In the second stage,a nonlinear fractional programming based power control algorithm is developed to maximize then energy efficiency of the base station.Finally,the proposed algorithm is evaluated based on real-world road topology and realistic vehicular traffic.Numerical results demonstrate that the proposed algorithm achieves superior performance in terms of energy efficiency and network coverage compared to other heuristic algorithms.