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.

翻译：融合深度神经网络的移动边缘缓存技术是一项具有显著潜力的创新技术，能够大幅降低用户延迟，适用于下一代无线网络。然而，MEC网络的有效性高度依赖于其预测并动态更新缓存节点中存储最流行内容的能力。为了有效运行，基于深度神经网络的流行度预测模型需要具备理解内容历史请求模式的能力，包括其时序和空间相关性。现有的先进时序DNN模型通过将多个内容的顺序请求模式同时输入网络来捕捉这些相关性，这显著增加了输入样本的规模。这促使我们通过提出一种基于对比输入样本思想的DNN流行度预测框架来应对这一挑战，该框架专为无人机辅助的MEC网络设计。所提出的架构被称为基于对比学习的生存分析方法，包含一个自监督对比学习模型，其中使用长短期记忆网络作为CL架构的编码器来学习顺序请求的时序信息。随后通过一个生存分析网络，CLSA架构的输出为每个内容未来流行度的概率，这些概率按降序排列以识别Top-K流行内容。仿真结果表明，所提出的CLSA架构在分类准确率和缓存命中率方面均优于其对应方法。