Transfer learning-based accelerated deep reinforcement learning for 5G RAN slicing
AM Nagib, H Abou-Zeid… - 2021 IEEE 46th …, 2021 - ieeexplore.ieee.org
Deep Reinforcement Learning (DRL) algorithms have been recently proposed to solve
dynamic Radio Resource Management (RRM) problems in 5G networks. However, the slow …
dynamic Radio Resource Management (RRM) problems in 5G networks. However, the slow …
Accelerating reinforcement learning via predictive policy transfer in 6G RAN slicing
AM Nagib, H Abou-Zeid… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Reinforcement Learning (RL) algorithms have recently been proposed to solve dynamic
radio resource management (RRM) problems in beyond 5G networks. However, RL-based …
radio resource management (RRM) problems in beyond 5G networks. However, RL-based …
Deep Reinforcement Learning for Scalable Dynamic Bandwidth Allocation in RAN Slicing with Highly Mobile Users
Radio Access Network (RAN) slicing is a key technology in 5G communication systems. It
dynamically allocates network resources such as bandwidth and time slots to each RAN …
dynamically allocates network resources such as bandwidth and time slots to each RAN …
Dynamic SDN-based radio access network slicing with deep reinforcement learning for URLLC and eMBB services
A Filali, Z Mlika, S Cherkaoui… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Radio access network (RAN) slicing is a key technology that enables 5G network to support
heterogeneous requirements of generic services, namely ultra-reliable low-latency …
heterogeneous requirements of generic services, namely ultra-reliable low-latency …
Advancing RAN slicing with offline reinforcement learning
Dynamic radio resource management (RRM) in wireless networks presents significant
challenges, particularly in the context of Radio Access Network (RAN) slicing. This …
challenges, particularly in the context of Radio Access Network (RAN) slicing. This …
Reinforcement learning-based radio access network slicing for a 5G system with support for cellular V2X
HDR Albonda, J Pérez-Romero - … 2019, Poznan, Poland, June 11–12 …, 2019 - Springer
Abstract 5G mobile systems are expected to host a variety of services and applications such
as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) …
as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) …
Explanation-Guided Deep Reinforcement Learning for Trustworthy 6G RAN Slicing
F Rezazadeh, H Chergui… - 2023 IEEE …, 2023 - ieeexplore.ieee.org
The complexity of emerging sixth-generation (6G) wireless networks has sparked an
upsurge in adopting artificial intelligence (AI) to underpin the challenges in network …
upsurge in adopting artificial intelligence (AI) to underpin the challenges in network …
Reinforcement learning for radio resource management in ran slicing: A survey
M Zangooei, N Saha, M Golkarifard… - IEEE Communications …, 2023 - ieeexplore.ieee.org
Dynamic radio resource allocation to network slices in mobile networks is challenging due to
the diverse requirements of RAN slices and the dynamic environment of wireless networks …
the diverse requirements of RAN slices and the dynamic environment of wireless networks …
Joint multi-objective optimization for radio access network slicing using multi-agent deep reinforcement learning
Radio access network (RAN) slices can provide various customized services for next-
generation wireless networks. Thus, multiple performance metrics of different types of RAN …
generation wireless networks. Thus, multiple performance metrics of different types of RAN …
Safe and accelerated deep reinforcement learning-based O-RAN slicing: A hybrid transfer learning approach
AM Nagib, H Abou-Zeid… - IEEE Journal on Selected …, 2023 - ieeexplore.ieee.org
The open radio access network (O-RAN) architecture supports intelligent network control
algorithms as one of its core capabilities. Data-driven applications incorporate such …
algorithms as one of its core capabilities. Data-driven applications incorporate such …