Densification of network base stations is indispensable to achieve the stringent Quality of
Service (QoS) requirements of future mobile networks. However, with a dense deployment of …

With the advent of new technologies in both hardware and software, we are in the need of a
new type of application that requires huge computation power and minimal delay …

Combinatorial optimization problems are typically tackled by the branch-and-bound
paradigm. We propose a new graph convolutional neural network model for learning branch …

With the development of recent innovative applications (eg, augment reality, self-driving, and
various cognitive applications), more and more computation-intensive and data-intensive …

Mobile-edge computing (MEC) is an emerging paradigm that provides a capillary
distribution of cloud computing capabilities to the edge of the wireless access network …

Resource allocation plays a pivotal role in improving the performance of wireless and
communication networks. However, the optimization of resource allocation is typically …

Vehicular Edge Computing (VEC) is expected to be an effective solution to meet the ultra-
low delay requirements of many emerging Internet of Vehicles (IoV) services by shifting the …

With the explosive growth of diverse Internet of Things (IoT) applications, mobile edge
computing (MEC) has been brought to settle the conflict between computation-intensive …

In the past few years, there has been an explosive surge in the use of machine learning (ML)
techniques to address combinatorial optimization (CO) problems, especially mixed-integer …

This study explores a Robust, Risk-aware, Resilient, and Sustainable Closed-Loop Supply
Chain Network Design (3RSCLSCND) to tackle demand fluctuation like COVID-19 …