基于位置预测的认知车联网协作频谱感知算法

针对认知车联网中由地形起伏或密集城市结构而引起的频谱感知性能低、延时大等问题,提出了一种基于位置预测的协作频谱感知算法。首先,采用能量检测法进行本地频谱感知,通过将次用户接收到的信号能量大小和预先设定的阈值进行比较,初步判定频谱是否被主用户占用。然后,利用认知车联网中车辆位置预测技术,计算车辆位置和信道状态信息,并据此设置置信值,删除置信值低的次用户。最后,在融合中心采用加入置信值的似然比融合规则得到最终判决结果。仿真结果表明,与传统算法相比,所提算法能有效提高频谱感知性能并缩短感知时间,尤其适用于对实时性要求较高的车联网系统。

Cooperative Spectrum Sensing Algorithm Based on Location-Predict in Cognitive Internet of Vehicles

An optimized cooperative spectrum sensing algorithm based on location prediction is proposed to solve the problems of low spectrum sensing performance and large delay caused by topographic relief or dense urban structures in cognitive Internet of vehicles (IoV).Firstly,the local spectrum sensing is performed by the energy detection method.By comparing the signal energy received by the secondary users with the predefined threshold value,whether the spectrum is occupied by the primary users can be identified preliminarily.Then,by using the vehicle location prediction technology in the cognitive IoV,the vehicle position and channel state information are calculated to set confidence values,and the secondary users with low confidence values are removed.Finally,the final decision is obtained by using the likelihood ratio fusion rule with confidence values in the fusion center.The simulation results show that compared with the traditional algorithm,the proposed algorithm can effectively improve the spectrum sensing performance and shorten the spectrum sensing time,which is especially suitable for the real-time IoV.