Joint load control and energy sharing for renewable powered small base stations: A machine learning approach

N Piovesan, D López-Pérez… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The deployment of dense networks of small base stations represents one of the most
promising solutions for future mobile networks to meet the foreseen increasing traffic …

Reinforcement learning for traffic-adaptive sleep mode management in 5G networks

M Masoudi, MG Khafagy, E Soroush… - 2020 IEEE 31st …, 2020 - ieeexplore.ieee.org
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 …

A Base Station Sleeping Strategy in Heterogeneous Cellular Networks Based on User Traffic Prediction

X Wang, B Lyu, C Guo, J Xu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Real-time traffic in a cellular network varies over time and often shows tidal patterns, such as
the day/night traffic pattern. With this characteristic, we can reduce the energy consumption …

Accurate load prediction algorithms assisted with machine learning for network traffic

Y Gao, M Zhang, J Chen, J Han, D Li… - … and Mobile Computing …, 2021 - ieeexplore.ieee.org
With the increasingly higher demand on radio access networks, problems such as serious
energy consumption and network load imbalance have aroused, catching more attention …

Reinforcement learning based dynamic function splitting in disaggregated green open RANs

T Pamuklu, M Erol-Kantarci… - ICC 2021-IEEE …, 2021 - ieeexplore.ieee.org
With the growing momentum around Open RAN (O-RAN) initiatives, performing dynamic
Function Splitting (FS) in disaggregated and virtualized Radio Access Networks (vRANs), in …

Reinforcement learning approach for advanced sleep modes management in 5G networks

FE Salem, Z Altman, A Gati, T Chahed… - 2018 IEEE 88th …, 2018 - ieeexplore.ieee.org
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 …

TACT: A transfer actor-critic learning framework for energy saving in cellular radio access networks

R Li, Z Zhao, X Chen, J Palicot… - IEEE transactions on …, 2014 - ieeexplore.ieee.org
Recent works have validated the possibility of improving energy efficiency in radio access
networks (RANs), achieved by dynamically turning on/off some base stations (BSs). In this …

Renewable energy management in cellular networks: An online strategy based on ARIMA forecasting and a Markov chain model

J Leithon, TJ Lim, S Sun - 2016 IEEE Wireless Communications …, 2016 - ieeexplore.ieee.org
In this paper, we propose an online energy management strategy to minimize the
operational expenses incurred by cellular base stations powered by both renewable and …

Energy efficiency of 5G mobile networks with base station sleep modes

P Lähdekorpi, M Hronec, P Jolma… - 2017 IEEE Conference …, 2017 - ieeexplore.ieee.org
The paper presents system level simulation results on future base station energy saving
using a time-triggered sleep model. The energy efficiency of future base station is compared …

Optimal online control for sleep mode in green base stations

R Combes, SE Elayoubi, A Ali, L Saker, T Chahed - Computer Networks, 2015 - Elsevier
In this paper, we investigate network sleep mode schemes for reducing energy consumption
of radio access networks. We first propose, using Markov Decision Processes (MDPs), an …