Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories
Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the
central network, incorporating edge nodes close to end devices. This expansion facilitates …
central network, incorporating edge nodes close to end devices. This expansion facilitates …
The frontiers of deep reinforcement learning for resource management in future wireless HetNets: Techniques, challenges, and research directions
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
An overview of intelligent wireless communications using deep reinforcement learning
Future wireless communication networks tend to be intelligentized to accomplish the
missions that cannot be preprogrammed. In the new intelligent communication systems …
missions that cannot be preprogrammed. In the new intelligent communication systems …
A survey on how network simulators serve reinforcement learning in wireless networks
S Ergun, I Sammour, G Chalhoub - Computer Networks, 2023 - Elsevier
Rapid adoption of mobile devices, coupled with the increase in prominence of mobile
applications and services, resulted in unprecedented infrastructure requirements for mobile …
applications and services, resulted in unprecedented infrastructure requirements for mobile …
Optimization for reinforcement learning: From a single agent to cooperative agents
Fueled by recent advances in deep neural networks, reinforcement learning (RL) has been
in the limelight because of many recent breakthroughs in artificial intelligence, including …
in the limelight because of many recent breakthroughs in artificial intelligence, including …
Cost-efficient federated reinforcement learning-based network routing for wireless networks
Z Abou El Houda, D Nabousli… - 2022 IEEE Future …, 2022 - ieeexplore.ieee.org
Advances in Artificial Intelligence (AI) provide new capabilities to handle network routing
problems. However, the lack of up-to-date training data, slow convergence, and low …
problems. However, the lack of up-to-date training data, slow convergence, and low …
Enhanced off-policy reinforcement learning with focused experience replay
SH Kong, IMA Nahrendra, DH Paek - IEEE Access, 2021 - ieeexplore.ieee.org
Utilizing the collected experience tuples in the replay buffer (RB) is the primary way of
exploiting the experiences in the off-policy reinforcement learning (RL) algorithms, and …
exploiting the experiences in the off-policy reinforcement learning (RL) algorithms, and …
Towards self-driving radios: Physical-layer control using deep reinforcement learning
Modern radios, such as 5G New Radio, feature a large set of physical-layer control knobs in
order to support an increasing number of communication scenarios spanning multiple use …
order to support an increasing number of communication scenarios spanning multiple use …
EXPLORA: AI/ML EXPLainability for the Open RAN
The Open Radio Access Network (RAN) paradigm is transforming cellular networks into a
system of disaggregated, virtualized, and software-based components. These self-optimize …
system of disaggregated, virtualized, and software-based components. These self-optimize …
Application of reinforcement learning to wireless sensor networks: models and algorithms
Wireless sensor network (WSN) consists of a large number of sensors and sink nodes which
are used to monitor events or environmental parameters, such as movement, temperature …
are used to monitor events or environmental parameters, such as movement, temperature …