Open AccessDRX‐based energy‐efficient supervised machine learning algorithm for mobile communication networks
Author(s) -
RuízGuirola David E.,
RodríguezLópez Carlos A.,
MontejoSánchez Samuel,
Souza Richard Demo,
Imran Muhammad Ali
Publication year - 2021
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/cmu2.12137
Subject(s) - computer science , quality of experience , energy consumption , voice over ip , energy (signal processing) , user equipment , efficient energy use , real time computing , computer network , algorithm , quality of service , the internet , base station , ecology , statistics , mathematics , world wide web , electrical engineering , biology , engineering
The continuous traffic increase of mobile communication systems has the collateral effect of higher energy consumption, affecting battery lifetime in the user equipment (UE). An effective solution for energy saving is to implement a discontinuous reception (DRX) mode. However, guaranteeing a desired quality of experience (QoE) while simultaneously saving energy is a challenge; but undoubtedly both energy efficiency and the QoE have been essential aspects for the provision of real‐time services, such as voice over Internet protocol (VoIP), voice over LTE, and mobile broadband in 4G networks and beyond. This paper focuses on human voice communications and proposes a Gaussian process regression algorithm that is capable of recognizing patterns of silence and predicts its duration in human conversations, with a prediction error as low as 1.87%. The proposed machine learning mechanism saves energy by switching OFF/ON the radio frequency interface, in order to extend the UE autonomy without harming QoE. Simulation results validate the effectiveness of the proposed mechanism compared with the related literature, showing improvements in energy savings of more than 30% while ensuring a desired QoE level with low computational cost.