Abstract
Mobile Edge Caching (MEC) integrated with Deep Neural Networks (DNNs) is an innovative technology with significant potential for the future generation of wireless networks, resulting in a considerable reduction in users' latency. The MEC network's effectiveness, however, heavily relies on its capacity to predict and dynamically update the storage of caching nodes with the most popular contents. To be effective, a DNN-based popularity prediction model needs to have the ability to understand the historical request patterns of content, including their temporal and spatial correlations. Existing state-of-the-art time-series DNN models capture the latter by simultaneously inputting the sequential request patterns of multiple contents to the network, considerably increasing the size of the input sample. This motivates us to address this challenge by proposing a DNN-based popularity prediction framework based on the idea of contrasting input samples against each other, designed for the Unmanned Aerial Vehicle (UAV)-aided MEC networks. Referred to as the Contrastive Learning-based Survival Analysis (CLSA), the proposed architecture consists of a self-supervised Contrastive Learning (CL) model, where the temporal information of sequential requests is learned using a Long Short Term Memory (LSTM) network as the encoder of the CL architecture. Followed by a Survival Analysis (SA) network, the output of the proposed CLSA architecture is probabilities for each content's future popularity, which are then sorted in descending order to identify the Top-K popular contents. Based on the simulation results, the proposed CLSA architecture outperforms its counterparts across the classification accuracy and cache-hit ratio.
Abstract (translated)
Mobile Edge caching (MEC) 与深度神经网络(DNN)集成是一项具有对未来无线网络潜在重要潜力的创新技术,其结果能够显著降低用户的延迟。然而,MEC 网络的有效性很大程度上依赖于其能够预测并动态更新最流行内容缓存节点的存储。要有效,一个基于 DNN 的流行度预测模型需要能够理解内容的历史请求模式,包括其时间轴和空间相关性。现有的先进的时间序列 DNN 模型通过同时输入多个内容的不同请求模式,能够捕捉后者,显著增加了输入样本的大小。这激励我们提出一个基于比较输入样本彼此对抗的想法设计的 DNN 流行度预测框架,该框架被称为Contrastive Learning-based survival Analysis (CLSA),它由一个自监督的Contrastive Learning (CL)模型组成,其中使用一个长短期记忆(LSTM)网络作为 CL 架构的编码器,学习Sequential Requests 的时间信息,然后使用生存分析(SA)网络排序,以确定前 K 流行内容。根据模拟结果,该提出的 CLSA 架构在分类准确率和缓存击中率方面都表现优异。
URL
https://arxiv.org/abs/2303.12097
