10th ITU Academic Conference Kaleidoscope: Machine Learning for a 5G Future, ITU K 2018
- Fecha: 31 diciembre 2018
- ISBN: 9789261269210
- Tipo de fuente: Ponencia
- DOI: 10.23919/ITU-WT.2018.8597940
- Tipo de documento: Documento de conferencia
- Editorial: Institute of Electrical and Electronics Engineers Inc.
© IEEE-CONFERENCE. All rights reserved. Smart Cities and Smart Industries are the flagships of the future IoT due to their potential to revolutionize the way in which people live and produce in advanced societies. In these two scenarios, a robust and ubiquitous communication infrastructure is needed to accommodate the traffic generated by the 10 billion devices that are expected by the year 2020. Due to its future world-wide presence, 5G is called to be this enabling technology. However, 5G is not a perfect solution, thus providing IoT nodes with different Radio Access Technologies (RATs) would allow them to exploit the various benefits offered by each RAT (such as lower power consumption or reduced operational costs). By making use of the mathematical framework of Reinforcement Learning, we have formulated the problem of deciding which RAT should an IoT node employ when reporting events. These so-called transmission policies maximize a predefined reward closely related to classical throughput while keeping power consumption and operational costs below a certain limit. A set of simulations are performed for IoT nodes provided with two RATs: LoRa and 5G. The results obtained are compared to those achieved under other intuitive policies to further highlight the benefits of our proposal.