Fast beamforming design via deep learning
H Huang, Y Peng, J Yang, W Xia… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Beamforming is considered as one of the most important techniques for designing advanced
multiple-input and multiple-output (MIMO) systems. Among existing design criterions, sum …
multiple-input and multiple-output (MIMO) systems. Among existing design criterions, sum …
Mobile edge computing-enabled internet of vehicles: Toward energy-efficient scheduling
Although modern transportation systems facilitate the daily life of citizens, the ever-
increasing energy consumption and air pollution challenge the establishment of green cities …
increasing energy consumption and air pollution challenge the establishment of green cities …
AI-enabled secure microservices in edge computing: Opportunities and challenges
F Al-Doghman, N Moustafa, I Khalil… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
The paradigm of edge computing has formed an innovative scope within the domain of the
Internet of Things (IoT) through expanding the services of the cloud to the network edge to …
Internet of Things (IoT) through expanding the services of the cloud to the network edge to …
Machine learning meets computation and communication control in evolving edge and cloud: Challenges and future perspective
Mobile Edge Computing (MEC) is considered an essential future service for the
implementation of 5G networks and the Internet of Things, as it is the best method of …
implementation of 5G networks and the Internet of Things, as it is the best method of …
Deep reinforcement learning for dynamic uplink/downlink resource allocation in high mobility 5G HetNet
Recently, the 5G is widely deployed for supporting communications of high mobility nodes
including train, vehicular and unmanned aerial vehicles (UAVs) largely emerged as the …
including train, vehicular and unmanned aerial vehicles (UAVs) largely emerged as the …
NOMA-assisted multi-access mobile edge computing: A joint optimization of computation offloading and time allocation
Multi-access mobile edge computing (MEC), which enables mobile users (MUs) to offload
their computation-workloads to the computation-servers located at the edge of cellular …
their computation-workloads to the computation-servers located at the edge of cellular …
Robust mobile crowd sensing: When deep learning meets edge computing
The emergence of MCS technologies provides a cost-efficient solution to accommodate
large-scale sensing tasks. However, despite the potential benefits of MCS, there are several …
large-scale sensing tasks. However, despite the potential benefits of MCS, there are several …
Deep cognitive perspective: Resource allocation for NOMA-based heterogeneous IoT with imperfect SIC
The Internet of Things (IoT) has attracted significant attentions in the fifth generation mobile
networks and the smart cities. However, considering the large numbers of connectivity …
networks and the smart cities. However, considering the large numbers of connectivity …
Routing or computing? The paradigm shift towards intelligent computer network packet transmission based on deep learning
Recent years, Software Defined Routers (SDRs)(programmable routers) have emerged as a
viable solution to provide a cost-effective packet processing platform with easy extensibility …
viable solution to provide a cost-effective packet processing platform with easy extensibility …
A hybrid secure routing and monitoring mechanism in IoT-based wireless sensor networks
BD Deebak, F Al-Turjman - Ad Hoc Networks, 2020 - Elsevier
Abstract Internet of Things (IoT) has advanced its pervasiveness across the globe for the
development of smart networks. It is aimed to deploy network edge that enables smart …
development of smart networks. It is aimed to deploy network edge that enables smart …