Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks
Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment
costs, which have been considered to be a promising technique in the next-generation …
costs, which have been considered to be a promising technique in the next-generation …
A survey on resource allocation for 5G heterogeneous networks: Current research, future trends, and challenges
In the fifth-generation (5G) mobile communication system, various service requirements of
different communication environments are expected to be satisfied. As a new evolution …
different communication environments are expected to be satisfied. As a new evolution …
A novel duplex deep reinforcement learning based RRM framework for next-generation V2X communication networks
Resource management in the next-generation vehicle-to-everything (V2X) communication
networks is a demanding research problem. It is difficult to achieve the best results if the …
networks is a demanding research problem. It is difficult to achieve the best results if the …
Deep reinforcement learning based wireless network optimization: A comparative study
There is a growing interest in applying deep reinforcement learning (DRL) methods to
optimizing the operation of wireless networks. In this paper, we compare three state of the art …
optimizing the operation of wireless networks. In this paper, we compare three state of the art …
User access control in open radio access networks: A federated deep reinforcement learning approach
Targeting at implementing the next generation radio access networks (RANs) with
virtualized network components, the open RAN (O-RAN) has been regarded as a novel …
virtualized network components, the open RAN (O-RAN) has been regarded as a novel …
vrAIn: A deep learning approach tailoring computing and radio resources in virtualized RANs
JA Ayala-Romero, A Garcia-Saavedra… - The 25th Annual …, 2019 - dl.acm.org
The virtualization of radio access networks (vRAN) is the last milestone in the NFV
revolution. However, the complex dependencies between computing and radio resources …
revolution. However, the complex dependencies between computing and radio resources …
A Comparative Analysis of Deep Reinforcement Learning-based xApps in O-RAN
The highly heterogeneous ecosystem of Next Generation (NextG) wireless communication
systems calls for novel networking paradigms where functionalities and operations can be …
systems calls for novel networking paradigms where functionalities and operations can be …
Applications of deep reinforcement learning in communications and networking: A survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
Deep reinforcement learning for computation and communication resource allocation in multiaccess MEC assisted railway IoT networks
J Xu, B Ai, L Chen, Y Cui… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Multi-access mobile edge computing (MEC) is envisioned as a key enabling technology to
support compute-intensive and delay-sensitive applications in railway Internet of Things …
support compute-intensive and delay-sensitive applications in railway Internet of Things …
RAN resource slicing in 5G using multi-agent correlated Q-learning
5G is regarded as a revolutionary mobile network, which is expected to satisfy a vast number
of novel services, ranging from remote health care to smart cities. However, heterogeneous …
of novel services, ranging from remote health care to smart cities. However, heterogeneous …