Intelligent user association for symbiotic radio networks using deep reinforcement learning
In this paper, we are interested in symbiotic radio networks (SRNs), in which an Internet-of-
Things (IoT) network parasitizes in a primary cellular network to achieve spectrum-, energy …
Things (IoT) network parasitizes in a primary cellular network to achieve spectrum-, energy …
Meta federated reinforcement learning for distributed resource allocation
In cellular networks, resource allocation is usually performed in a centralized way, which
brings huge computation complexity to the base station (BS) and high transmission …
brings huge computation complexity to the base station (BS) and high transmission …
Multi-agent team learning in virtualized open radio access networks (o-ran)
Starting from the concept of the Cloud Radio Access Network (C-RAN), continuing with the
virtual Radio Access Network (vRAN) and most recently with the Open RAN (O-RAN) …
virtual Radio Access Network (vRAN) and most recently with the Open RAN (O-RAN) …
RIS-aided proactive mobile network downlink interference suppression: A deep reinforcement learning approach
A proactive mobile network (PMN) is a novel architecture enabling extremely low-latency
communication. This architecture employs an open-loop transmission mode that prohibits all …
communication. This architecture employs an open-loop transmission mode that prohibits all …
Double deep Q-network-based energy-efficient resource allocation in cloud radio access network
Cloud radio access network (CRAN) has been shown as an effective means to boost
network performance. Such gain stems from the intelligent management of remote radio …
network performance. Such gain stems from the intelligent management of remote radio …
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 …
Bayesian online learning for energy-aware resource orchestration in virtualized RANs
JA Ayala-Romero, A Garcia-Saavedra… - … -IEEE Conference on …, 2021 - ieeexplore.ieee.org
Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio
stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms …
stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms …
Optimization of URLLC and eMBB multiplexing via deep reinforcement learning
Y Li, C Hu, J Wang, M Xu - 2019 IEEE/CIC International …, 2019 - ieeexplore.ieee.org
In 5G mobile networks, multiple scenarios have emerged to meet different services
requirement. The limited spectrum resource becoming more and more crowed to meet …
requirement. The limited spectrum resource becoming more and more crowed to meet …
Deep learning for B5G open radio access network: Evolution, survey, case studies, and challenges
Open Radio Access Network (O-RAN) alliance was recently launched to devise a new RAN
architecture featuring open, software-driven, virtual, and intelligent radio access architecture …
architecture featuring open, software-driven, virtual, and intelligent radio access architecture …
EdgeRIC: Empowering realtime intelligent optimization and control in NextG networks
Radio Access Networks (RAN) are increasingly softwarized and accessible via data-
collection and control interfaces. RAN intelligent control (RIC) is an approach to manage …
collection and control interfaces. RAN intelligent control (RIC) is an approach to manage …