软件定义车联网中缓存辅助的NOMA功率分配方案研究

Research on cache aided NOMA power allocation scheme in software defined vehicular network

由于对丰富多媒体服务的需求日益增长,车联网需要提供海量的设备连接以满足高频谱效率和低延迟的需求。软件定义网络(SDN)、缓存和非正交多址接入(NOMA)被认为是有效解决这些关键挑战的潜在技术。针对软件定义车联网,提出了一种缓存辅助的NOMA功率分配方案。首先,针对车联网中车辆总是处于高速运动状态的特点,提出了一种新的簇头选择算法,到达的道路交通将借助SDN进行预测,实现自适应车辆分簇。其次,引入了缓存辅助的NOMA方案,每个车辆在文件缓存阶段使用NOMA原理缓存和请求文件。再次,针对双Nakagami-m衰落条件下的两个簇头车辆通信场景,提出了一种最优功率分配策略,将优化问题公式转换为找到每辆车的最佳功率曲线,从而最大化地在每辆车上成功解码目标文件的概率。最后,数值仿真和理论分析表明,所提缓存辅助NOMA功率分配方案的性能明显优于传统的NOMA和缓存辅助的OMA。

Due to the increasing demand for rich multimedia services,Internet of Vehicles needs to provide massive equipment connections to meet the needs of high spectrum efficiency and low delay.SDN,caching and NOMA technologies are considered as potential solutions to effectively solve these key challenges.Therefore,this paper proposed a cache aided NOMA power allocation scheme for software defined vehicular network.Firstly,according to the characteristics of vehicles always moving at high speed in Internet of Vehicles,the scheme proposed a new cluster head selection algorithm.The arriving road traffic would be predicted with the help of SDN to realize adaptive vehicle clustering.Secondly,the scheme introduced a cache aided NOMA strategy.Each vehicle used NOMA principle to cache and request files in the file cache stage.Thirdly,for the two cluster head vehicle communication scenarios under the condition of double Nakagami-m fading,the scheme proposed an optimal power allocation strategy.The optimization problem was formulated as finding the optimal power curve of each vehicle,so as to maximize the probability of successfully decoding the target file on each vehicle.Finally,numerical simulation and theoretical analysis show that the performance of the proposed cache aided NOMA power allocation scheme is significantly better than the conventional NOMA and cache aided OMA.