  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.
