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 …
A survey on sleep mode techniques for ultra-dense networks in 5G and beyond
The proliferation of mobile users with an attendant rise in energy consumption mainly at the
base station has requested new ways of achieving energy efficiency in cellular networks …
base station has requested new ways of achieving energy efficiency in cellular networks …
Energy optimization with multi-slee** control in 5G heterogeneous networks using reinforcement learning
The massive deployment of small cells in 5G networks represents an alternative to meet the
ever increasing mobile data traffic and to provide very-high throughout by bringing the users …
ever increasing mobile data traffic and to provide very-high throughout by bringing the users …
Using reinforcement learning to reduce energy consumption of ultra-dense networks with 5G use cases requirements
In mobile networks, 5G Ultra-Dense Networks (UDNs) have emerged as they effectively
increase the network capacity due to cell splitting and densification. A Base Station (BS) is a …
increase the network capacity due to cell splitting and densification. A Base Station (BS) is a …
Trading off delay and energy saving through Advanced Sleep Modes in 5G RANs
While designed for being energy efficient, the deployment of 5G networks will further
increase Radio Access Networks (RANs) energy consumption with the twofold effect to raise …
increase Radio Access Networks (RANs) energy consumption with the twofold effect to raise …
Energy optimization in ultra-dense radio access networks via traffic-aware cell switching
We propose a reinforcement learning-based cell switching algorithm to minimize the energy
consumption in ultra-dense deployments without compromising the quality of service (QoS) …
consumption in ultra-dense deployments without compromising the quality of service (QoS) …
Digital twin assisted risk-aware sleep mode management using deep Q-networks
Base stations (BSs) are the most energy-consuming segment of mobile networks. To reduce
the energy consumption of BSs, inactive components of BSs, with a certain …
the energy consumption of BSs, inactive components of BSs, with a certain …
An analytical energy performance evaluation methodology for 5G base stations
The implementation of various base station (BS) energy saving (ES) features and the widely
varying network traffic demand makes it imperative to quantitatively evaluate the energy …
varying network traffic demand makes it imperative to quantitatively evaluate the energy …
Q-learning based radio resource adaptation for improved energy performance of 5G base stations
Radio resource adaptation (RRA) is an effective strategy to reduce the energy consumption
(EC) of a base station (BS) under variable input traffic demand. By combining RRA with …
(EC) of a base station (BS) under variable input traffic demand. By combining RRA with …
Optimal policies of advanced sleep modes for energy-efficient 5G networks
We study in this paper optimal control strategy for Advanced Sleep Modes (ASM) in 5G
networks. ASM correspond to different levels of sleep modes ranging from deactivation of …
networks. ASM correspond to different levels of sleep modes ranging from deactivation of …