TY - GEN
T1 - Enhancing coverage in narrow band-IoT using machine learning
AU - Chafii, Marwa
AU - Bader, Faouzi
AU - Palicot, Jacques
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/8
Y1 - 2018/6/8
N2 - Narrow Band-Internet of Thing (NB-IoT) is a recently proposed technology by 3GPP in Release-13. It provides low energy consumption and wide coverage in order to meet the requirements of its diverse applications that span social, industrial and environmental aspects. Increasing the number of repetitions of the transmission has been selected as a promising approach to enhance the coverage in NB-IoT up to 164 dB in terms of maximum coupling loss for uplink transmissions, which is a significant improvement compared with legacy LTE technologies, especially to serve users in deep coverage. However, a large number of repetitions reduces the system throughput and increases the energy consumption of the IoT devices, which reduces their battery lifetime and increases their maintenance cost. In this work, we propose a new method for enhancing the NB-IoT coverage based on machine learning algorithms. Instead of employing a random spectrum access procedure, dynamic spectrum access can reduce the number of required repetitions, increase the coverage, and reduce the energy consumption.
AB - Narrow Band-Internet of Thing (NB-IoT) is a recently proposed technology by 3GPP in Release-13. It provides low energy consumption and wide coverage in order to meet the requirements of its diverse applications that span social, industrial and environmental aspects. Increasing the number of repetitions of the transmission has been selected as a promising approach to enhance the coverage in NB-IoT up to 164 dB in terms of maximum coupling loss for uplink transmissions, which is a significant improvement compared with legacy LTE technologies, especially to serve users in deep coverage. However, a large number of repetitions reduces the system throughput and increases the energy consumption of the IoT devices, which reduces their battery lifetime and increases their maintenance cost. In this work, we propose a new method for enhancing the NB-IoT coverage based on machine learning algorithms. Instead of employing a random spectrum access procedure, dynamic spectrum access can reduce the number of required repetitions, increase the coverage, and reduce the energy consumption.
KW - Coverage Enhancement (CE)
KW - Dynamic spectrum access
KW - Narrow-band Internet of Things (NB-IoT)
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85049219795&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049219795&partnerID=8YFLogxK
U2 - 10.1109/WCNC.2018.8377263
DO - 10.1109/WCNC.2018.8377263
M3 - Conference contribution
AN - SCOPUS:85049219795
T3 - IEEE Wireless Communications and Networking Conference, WCNC
SP - 1
EP - 6
BT - 2018 IEEE Wireless Communications and Networking Conference, WCNC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Wireless Communications and Networking Conference, WCNC 2018
Y2 - 15 April 2018 through 18 April 2018
ER -