Energy optimization with multi-sleeping 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 …
Reinforcement learning for delay-constrained energy-aware small cells with multi-sleeping control
In 5G networks, specific requirements are defined on the periodicity of Synchronization
Signaling (SS) bursts. This imposes a constraint on the maximum period a Base Station (BS) …
Signaling (SS) bursts. This imposes a constraint on the maximum period a Base Station (BS) …
Reinforcement learning for traffic-adaptive sleep mode management in 5G networks
In mobile networks, base stations (BSs) have the largest share in energy consumption. To
reduce BS energy consumption, BS components with similar (de) activation times can be …
reduce BS energy consumption, BS components with similar (de) activation times can be …
Location-aware sleep strategy for energy-delay tradeoffs in 5G with reinforcement learning
In this paper, we propose a sleep strategy for energy-efficient 5G Base Stations (BSs) with
multiple Sleep Mode (SM) levels to bring down energy consumption. Such management of …
multiple Sleep Mode (SM) levels to bring down energy consumption. Such management of …
A distributed Q-Learning approach for adaptive sleep modes in 5G networks
In 5G networks, specific requirements are defined on the periodicity of Synchronization
Signaling (SS) bursts. This imposes a constraint on the maximum period a Base Station (BS) …
Signaling (SS) bursts. This imposes a constraint on the maximum period a Base Station (BS) …
Reinforcement learning approach for advanced sleep modes management in 5G networks
Advanced Sleep Modes (ASMs) correspond to a gradual deactivation of the Base Station
(BS)'s components in order to reduce its Energy Consumption (EC). Different levels of Sleep …
(BS)'s components in order to reduce its Energy Consumption (EC). Different levels of Sleep …
Traffic-aware advanced sleep modes management in 5G networks
Advanced Sleep Modes (ASMs) are defined as a progressive shutdown of the Base Station
(BS) depending on the activation and the deactivation times of the different components …
(BS) depending on the activation and the deactivation times of the different components …
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 …
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 …
Load analysis and sleep mode optimization for energy-efficient 5G small cell networks
H Celebi, İ Güvenç - 2017 IEEE international conference on …, 2017 - ieeexplore.ieee.org
Dense deployment of small cells is seen as one of the major approaches for addressing the
traffic demands in next-generation wireless networks. However, dense deployment of large …
traffic demands in next-generation wireless networks. However, dense deployment of large …