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CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks
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.
ViT-CAT: Parallel Vision Transformers with Cross Attention Fusion for Popularity Prediction in MEC Networks
Followed by a Cross Attention (CA) module as the Fusion Center (FC), the proposed ViT-CAT is capable of learning the mutual information between temporal and spatial correlations, as well, resulting in improving the classification accuracy, and decreasing the model's complexity about 8 times.
Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks
Most existing datadriven popularity prediction models, however, are not suitable for the coded/uncoded content placement frameworks.
JUNO: Jump-Start Reinforcement Learning-based Node Selection for UWB Indoor Localization
Performance of UWB-based localization systems, however, can significantly degrade because of Non Line of Sight (NLoS) connections between a mobile user and UWB beacons.
DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection
Although UWB technology can enhance the accuracy of indoor positioning due to the use of a wide-frequency spectrum, there are key challenges ahead for its efficient implementation.
TB-ICT: A Trustworthy Blockchain-Enabled System for Indoor COVID-19 Contact Tracing
Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased to prevent spread of this highly contagious virus and be prepared for the potential future ones.
