Processing ANN traffic predictions for RAN energy efficiency
The field of networking, like many others, is experiencing a peak of interest in the use of
Machine Learning (ML) algorithms. In this paper, we focus on the application of ML tools to …
Machine Learning (ML) algorithms. In this paper, we focus on the application of ML tools to …
RAN energy efficiency and failure rate through ANN traffic predictions processing
In this paper, we focus on the application of ML tools to resource management in a portion of
a Radio Access Network (RAN) and, in particular, to Base Station (BS) activation and …
a Radio Access Network (RAN) and, in particular, to Base Station (BS) activation and …
Intelligent RAN Power Saving using Balanced Model Training in Cellular Networks
V Singh, M Gupta, C Maciocco - 2022 20th International …, 2022 - ieeexplore.ieee.org
optimizing power consumption of 5G systems and next generation technology deployments
is a critical problem. It is essential that the solution for optimizing power consumption takes …
is a critical problem. It is essential that the solution for optimizing power consumption takes …
Greener RAN operation through machine learning
The use of base station (BS) sleep modes is one of the most studied approaches for the
reduction of the energy consumption of radio access networks (RANs). Many papers have …
reduction of the energy consumption of radio access networks (RANs). Many papers have …
Neural networks for cellular base station switching
The adoption of base station sleep modes is considered one of the most effective
approaches for the reduction of the energy consumption of radio access networks. Sleep …
approaches for the reduction of the energy consumption of radio access networks. Sleep …
A predictive model for minimising power usage in radio access networks
E Carolan, SC McLoone, R Farrell - … 16-18, 2015, Revised Selected Papers …, 2015 - Springer
In radio access networks traffic load varies greatly both spatially and temporally. However,
resource usage of Base Stations (BSs) does not solely depend on the traffic load; auxiliary …
resource usage of Base Stations (BSs) does not solely depend on the traffic load; auxiliary …
Use of Machine Learning for energy efficiency in present and future mobile networks
D Sesto-Castilla, E Garcia-Villegas… - 2019 IEEE Wireless …, 2019 - ieeexplore.ieee.org
Given the current evolution trends in mobile cellular networks, which is approaching us
towards the future 5G paradigm, novel techniques for network management are in the …
towards the future 5G paradigm, novel techniques for network management are in the …
On the predictability of next generation mobile network traffic using artificial neural networks
I Loumiotis, E Adamopoulou… - International Journal …, 2015 - Wiley Online Library
Though the introduction of the new 4th Generation mobile access technologies promises to
satisfy the increasing bandwidth demand of the end‐users, it poses in parallel the need for …
satisfy the increasing bandwidth demand of the end‐users, it poses in parallel the need for …
AI-Ready Energy Modelling for Next Generation RAN
Recent sustainability drives place energy-consumption metrics in centre-stage for the design
of future radio access networks (RAN). At the same time, optimising the trade-off between …
of future radio access networks (RAN). At the same time, optimising the trade-off between …
Energy saving through a learning framework in greener cellular radio access networks
Recent works have validated the possibility of energy efficiency improvement in radio
access networks (RAN), depending on dynamically turn on/off some base stations (BSs). In …
access networks (RAN), depending on dynamically turn on/off some base stations (BSs). In …