3 research outputs found
DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection
Recent advancements in Internet of Things (IoTs) have brought about a surge
of interest in indoor positioning for the purpose of providing reliable,
accurate, and energy-efficient indoor navigation/localization systems. Ultra
Wide Band (UWB) technology has been emerged as a potential candidate to satisfy
the aforementioned requirements. 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. On the one
hand, achieving high precision in positioning relies on the
identification/mitigation Non Line of Sight (NLoS) links, leading to a
significant increase in the complexity of the localization framework. On the
other hand, UWB beacons have a limited battery life, which is especially
problematic in practical circumstances with certain beacons located in
strategic positions. To address these challenges, we introduce an efficient
node selection framework to enhance the location accuracy without using complex
NLoS mitigation methods, while maintaining a balance between the remaining
battery life of UWB beacons. Referred to as the Deep Q-Learning
Energy-optimized LoS/NLoS (DQLEL) UWB node selection framework, the mobile user
is autonomously trained to determine the optimal set of UWB beacons to be
localized based on the 2-D Time Difference of Arrival (TDoA) framework. The
effectiveness of the proposed DQLEL framework is evaluated in terms of the link
condition, the deviation of the remaining battery life of UWB beacons, location
error, and cumulative rewards. Based on the simulation results, the proposed
DQLEL framework significantly outperformed its counterparts across the
aforementioned aspects
ViT-CAT: Parallel Vision Transformers with Cross Attention Fusion for Popularity Prediction in MEC Networks
Mobile Edge Caching (MEC) is a revolutionary technology for the Sixth
Generation (6G) of wireless networks with the promise to significantly reduce
users' latency via offering storage capacities at the edge of the network. The
efficiency of the MEC network, however, critically depends on its ability to
dynamically predict/update the storage of caching nodes with the top-K popular
contents. Conventional statistical caching schemes are not robust to the
time-variant nature of the underlying pattern of content requests, resulting in
a surge of interest in using Deep Neural Networks (DNNs) for time-series
popularity prediction in MEC networks. However, existing DNN models within the
context of MEC fail to simultaneously capture both temporal correlations of
historical request patterns and the dependencies between multiple contents.
This necessitates an urgent quest to develop and design a new and innovative
popularity prediction architecture to tackle this critical challenge. The paper
addresses this gap by proposing a novel hybrid caching framework based on the
attention mechanism. Referred to as the parallel Vision Transformers with Cross
Attention (ViT-CAT) Fusion, the proposed architecture consists of two parallel
ViT networks, one for collecting temporal correlation, and the other for
capturing dependencies between different contents. 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. Based on the simulation
results, the proposed ViT-CAT architecture outperforms its counterparts across
the classification accuracy, complexity, and cache-hit ratio