EcoEdgeTwin: Enhanced 6G Network via Mobile Edge Computing and Digital Twin Integration
SH Karobi, S Ahmed, SR Sabuj, A Khokhar - arXiv preprint arXiv …, 2024 - arxiv.org
In the 6G era, integrating Mobile Edge Computing (MEC) and Digital Twin (DT) technologies
presents a transformative approach to enhance network performance through predictive …
presents a transformative approach to enhance network performance through predictive …
A federated deep learning empowered resource management method to optimize 5G and 6G quality of services (QoS)
H Alsulami, SH Serbaya… - Wireless …, 2022 - Wiley Online Library
The quality of service (QoS) in 5G/6G communication enormously depends upon the
mobility and agility of the network architecture. An increase in the possible uses of 5G …
mobility and agility of the network architecture. An increase in the possible uses of 5G …
Deep reinforcement learning-based RAN slicing for UL/DL decoupled cellular V2X
The emerging uplink (UL) and downlink (DL) decoupled radio access networks (RAN) has
attracted a lot of attention due to the significant gains in network throughput, load balancing …
attracted a lot of attention due to the significant gains in network throughput, load balancing …
On enabling 5G dynamic TDD by leveraging deep reinforcement learning and O-RAN
Dynamic Time Duplex Division (D-TDD) is a promising solution to accommodate the new
emerging 5G and 6G services characterised by asymmetric and dynamic Uplink (UL) and …
emerging 5G and 6G services characterised by asymmetric and dynamic Uplink (UL) and …
[PDF][PDF] AI-based Radio Resource and Transmission Opportunity Allocation for 5G-V2X HetNets: NR and NR-U networks
The capacity of fifth-generation(5G) vehicle-to-everything (V2X) networks poses significant
challenges. To address this challenge, this paper utilizes New Radio (NR) and New Radio …
challenges. To address this challenge, this paper utilizes New Radio (NR) and New Radio …
Energy Efficient Resource Allocation Framework Based on Dynamic Meta-Transfer Learning for V2X Communications
Most existing studies consider the deep reinforcement learning (DRL) based Q-learning
approach due to its ability to quickly converge to a near-optimal solution, resulting in …
approach due to its ability to quickly converge to a near-optimal solution, resulting in …
Deep reinforcement learning based algorithm for symbiotic radio iot throughput optimization in 6g network
GM Salama, SS Metwly, EG Shehata… - IEEE …, 2023 - ieeexplore.ieee.org
Internet of Things (IoT)-based 6G is expected to revolutionize our world. Various candidate
technologies have been proposed to meet IoT system requirements based on 6G, symbiotic …
technologies have been proposed to meet IoT system requirements based on 6G, symbiotic …
Deep learning for radio resource allocation with diverse quality-of-service requirements in 5G
To accommodate diverse Quality-of-Service (QoS) requirements in 5th generation cellular
networks, base stations need real-time optimization of radio resources in time-varying …
networks, base stations need real-time optimization of radio resources in time-varying …
Quality of service‐aware adaptive radio resource management based on deep federated Q‐learning for multi‐access edge computing in beyond 5G cloud‐radio …
N Kumar, A Ahmad - Transactions on Emerging …, 2023 - Wiley Online Library
Due to the exponential increase of mobile data services, both industry and academia have
turned their focus for improving the quality of service (QoS) provisioning for the beyond 5G …
turned their focus for improving the quality of service (QoS) provisioning for the beyond 5G …
Intelligent channel prediction and power adaptation in leo constellation for 6g
Integrated satellite and terrestrial networks (ISTN) are rapidly evolving to meet the ever-
increasing demands of higher throughput, lower latency, and wider coverage for future …
increasing demands of higher throughput, lower latency, and wider coverage for future …