5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network

B Priya, J Malhotra - Journal of ambient intelligence and humanized …, 2023 - Springer
The COVID-19 outbreak has stimulated the digital transformation of antiquated healthcare
system to a smart hospital, enabling the personalised and remote healthcare services. To …

Optimized intellectual resource scheduling using deep reinforcement Q‐learning in cloud computing

J Uma, P Vivekanandan… - Transactions on Emerging …, 2022 - Wiley Online Library
Cloud computing has recently attracted both academics and industrialists in the field of
research. Virtualization allows cloud service providers (CSPs) with their own data centers to …

Service-aware user association and resource allocation in integrated terrestrial and non-terrestrial networks: A genetic algorithm approach

DJ Birabwa, D Ramotsoela, N Ventura - IEEE Access, 2022 - ieeexplore.ieee.org
In 6G networks and beyond, multiple radio access networks (RANs), including; the satellite,
high altitude platforms, low altitude platforms, and the terrestrial network, will co-exist. These …

[HTML][HTML] Multi-agent deep reinforcement learning for user association and resource allocation in integrated terrestrial and non-terrestrial networks

DJ Birabwa, D Ramotsoela, N Ventura - Computer Networks, 2023 - Elsevier
Integrating the terrestrial network with non-terrestrial networks to provide radio access as
anticipated in the beyond 5G networks calls for efficient user association and resource …

Decentralized joint pilot and data power control based on deep reinforcement learning for the uplink of cell-free systems

IM Braga, RP Antonioli, G Fodor… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
While the problem of jointly controlling the pilot-and-data power in cell-based systems has
been extensively studied, this problem is difficult to solve in cell-free systems due to two …

QAAs: QoS provisioned artificial intelligence framework for AP selection in next-generation wireless networks

B Priya, J Malhotra - Telecommunication Systems, 2021 - Springer
Emerging trend of ubiquitous data access is driving the demand for effective wireless
communication connectivity. In essence to this, wireless local area network (WLAN) …

Multi-agent deep reinforcement learning based resource management in SWIPT enabled cellular networks with H2H/M2M co-existence

X Li, X Wei, S Chen, L Sun - Ad Hoc Networks, 2023 - Elsevier
Abstract Machine-to-Machine (M2M) communication is crucial in developing Internet of
Things (IoT). As it is well known that cellular networks have been considered as the primary …

Multiple QoS Enabled Intelligent Resource Management in Vehicle-to-Vehicle Communication

Y Deng, R Paul, YJ Choi - IEEE Transactions on Intelligent …, 2024 - ieeexplore.ieee.org
Vehicular networks have stringent quality of service (QoS) requirements in terms of
reliability, throughput, and latency. With the emergence of diverse services for autonomous …

Deep reinforcement learning based resource allocation approach for wireless networks considering network slicing paradigm

HHS Lopes, FGC Rocha, FHT Vieira - Journal of Communication …, 2023 - jcis.sbrt.org.br
In this paper, we present an approach for resource scheduling in wireless networks based
on the Network Slicing (NS) paradigm using Double Deep Q-Network (DDQN) …

Deep reinforcement learning for resource constrained multiclass scheduling in wireless networks

A Avranas, P Ciblat… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The problem of multiclass scheduling in a dynamic wireless setting is considered here,
where the available limited bandwidth resources are allocated to handle random service …