Rlops: Development life-cycle of reinforcement learning aided open ran
Radio access network (RAN) technologies continue to evolve, with Open RAN gaining the
most recent momentum. In the O-RAN specifications, the RAN intelligent controllers (RICs) …
most recent momentum. In the O-RAN specifications, the RAN intelligent controllers (RICs) …
Collaborative multi-BS power management for dense radio access network using deep reinforcement learning
Network energy efficiency is a main pillar in the design and operation of wireless
communication systems. In this paper, we investigate a dense radio access network (dense …
communication systems. In this paper, we investigate a dense radio access network (dense …
Semantic-aware collaborative deep reinforcement learning over wireless cellular networks
Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can
coordinate over a wireless network is a promising approach to enable future intelligent and …
coordinate over a wireless network is a promising approach to enable future intelligent and …
A deep-learning-based radio resource assignment technique for 5G ultra dense networks
Recently, deep learning has emerged as a state-of-the-art machine learning technique with
promising potential to drive significant breakthroughs in a wide range of research areas. The …
promising potential to drive significant breakthroughs in a wide range of research areas. The …
Dynamic CU-DU selection for resource allocation in O-RAN using actor-critic learning
S Mollahasani, M Erol-Kantarci… - 2021 IEEE Global …, 2021 - ieeexplore.ieee.org
Recently, there has been tremendous efforts by network operators and equipment vendors
to adopt intelligence and openness in the next generation radio access network (RAN). The …
to adopt intelligence and openness in the next generation radio access network (RAN). 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 …
Deep reinforcement learning-empowered resource allocation for mobile edge computing in cellular v2x networks
D Li, S Xu, P Li - Sensors, 2021 - mdpi.com
With the rapid development of vehicular networks, vehicle-to-everything (V2X)
communications have huge number of tasks to be calculated, which brings challenges to the …
communications have huge number of tasks to be calculated, which brings challenges to the …
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 …
Load-aware distributed resource allocation for MF-TDMA ad hoc networks: A multi-agent DRL approach
Without control center assistance, it is difficult that users take traffic loads of whole Ad Hoc
networks into account to utilize wireless resources collaboratively. Considering influence of …
networks into account to utilize wireless resources collaboratively. Considering influence of …
Deep reinforcement based optimization of function splitting in virtualized radio access networks
Virtualized Radio Access Network (vRAN) is one of the key enablers of future wireless
networks as it brings the agility to the radio access network (RAN) architecture and offers …
networks as it brings the agility to the radio access network (RAN) architecture and offers …