认知无线车载自组织网络中的联合路由调度

通过将认知无线电(cognitive radio, CR)技术应用到车载自组织网络(vehicular ad hoc networks, VANETs)(也称车联网)中，认知无线车载自组织网络(CR-VANETs)可以缓解频谱资源稀缺问题，有效提高车对车通信的频谱资源利用率.由于车辆的高速移动性以及认知无线电频谱资源的动态特性，使得传统的认知无线电网络或车载自组织网络中的路由协议无法直接应用到CR-VANETs中.目前，针对CR-VANETs的路由研究相对较少，如何最大效率地利用有限的频谱资源，同时降低跳数过多带来的频谱资源浪费，仍然是一个有待解决的问题.为此，提出了一种CR-VANETs中联合路由调度方案，结合了有限频谱资源调度研究与最小化路由跳数的优化目标.首先，建立了CR-VANETs中的网络模型和基于车对车通信的频谱感知模型，预测车辆间有效接触时间和频谱可用概率.其次，通过这些参数定义出通信链路消耗，并由此得出权衡链路质量的权重因子.通过分析优化目标，将其转化为有限频谱资源约束下的最小化路由跳数问题，并证明该问题为NP难问题.然后，针对这个联合路由调度问题提出一种混合启发式算法，结合了粒子群优化算法的快速收敛性和遗传算法的种群多样性，对有限频谱资源进行调度，同时优化路由跳数.最后仿真实验结果表明，与现有的CR-VANETs路由研究比较，有着更优的路由跳数并使其保持在一个相对稳定的值.

Joint Routing and Scheduling in Cognitive Radio Vehicular Ad Hoc Networks

Cognitive radio vehicular ad hoc networks (CR-VANETs) have been envisioned to solve the problem of spectrum scarcity and improved spectrum resource efficiency in vehicle-to-vehicle communication by exploiting cognitive radio into the vehicular ad hoc networks. Most existing routing protocols for cognitive radio networks or vehicular ad hoc networks cannot be applied to CR-VANETs directly due to the high-speed mobility of vehicles and dynamically changing availability of cognitive radio channels. At present, the routing research for CR-VANETs is relatively few. How to utilize the spectrum resources effectively and moreover reduce the spectrum band consumption caused by routing hops is still a pending problem. Aspiring to meet these demands and challenges, this paper presents a joint routing and scheduling, which combines the scheduling of spectrum resources and the goal of minimizing routing hops in CR-VANETs. To achieve this goal, we first establish a network model and a CR spectrum model to predict the contact duration between vehicles and the probability of spectrum availability. We define the communication link consumption and the weight of channel according to these parameters. Then we transform the optimization objective into a routing scheme with minimizing hop count, subject to constraint on the scheduling of spectrum resource, and moreover prove this routing scheme is NP-hard. To tackle this issue, a hybrid heuristic algorithm is composed by a particle swarm optimization with fast convergence and a genetic algorithm with population diversity. Simulation results demonstrate that our proposal provides better routing hop counts compared with other CR-VANETs protocols.