C-V2X边缘缓存中文件请求预测机制

在基于蜂窝通信演进形成的车用无线通信技术(Cellular-Vehicle to everything, C-V2X)场景下, 基站作为多接入边缘计算(Multi-access Edge Computing, MEC)边缘缓存节点可提高用户获取数据的效率, 但其缓存容量有限. 因此, C-V2X中如何准确预测缓存请求内容成为待解决的重要问题. 本文从文件请求的时变性出发, 针对实际的城市场景, 采用Simulation of Urban MObility (SUMO)对交通流进行建模; 其次, 通过采集实际网站分时分类的点击量数据, 并根据各路段交通流规律进行预处理, 构建用户请求模型; 最后, 利用Long Short-Term Memory (LSTM)深度学习模型进行训练, 预测各基站的文件请求. 仿真结果表明, 在网易新闻流行度分布和请求间隔分布形成的文件请求下, vanillaLSTM模型对娱乐类型数据集预测时的均方根误差在1.3左右.

Prediction Mechanism of File Requests for Edge Cache in C-V2X

In the scenario of Cellular-Vehicle to Everything (C-V2X) based on the evolution of cellular communication, a base station as a Multi-access Edge Computing (MEC) edge cache node can improve the efficiency of user data acquisition, but its cache capacity is limited. Therefore, accurate cache request content prediction in C-V2X has become an important issue that needs to be addressed. This article starts with the time-varying characteristics of file requests and uses the Simulation of Urban Mobility (SUMO) to analyze the traffic flow for actual urban scenario modeling. Secondly, collecting the traffic data of the actual website time-sharing classification, and pre-processing according to the traffic flow rules of each road section, and then the user request model is constructed and the law of the base station receiving data is revealed. The Long Short-Term Memory (LSTM) deep learning model trains and predicts the file requests that each base station will receive. The simulation results show that the root mean square error of the vanillaLSTM model in the entertainment data set is about 1.3 when the data set received by the base station is predicted under the file request formed by NetEase news popularity distribution and request interval distribution.