SmartCon: Deep Probabilistic Learning-Based Intelligent Link-Configuration in Narrowband-IoT Toward 5G and B5G

To enhance the coverage and transmission reliability, repetitions adopted by Narrowband Internet of Things (NB-IoT) allow repeating transmissions several times. However, this results in a waste of radio resources when the signal strength is high. In addition, in low signal quality, the selection of...

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Veröffentlicht in:IEEE transactions on cognitive communications and networking Jg. 8; H. 2; S. 1147 - 1158
Hauptverfasser: Karmakar, Raja, Kaddoum, Georges, Chattopadhyay, Samiran
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Configurations
Deep learning
Downlink
Dynamic scheduling
Generative adversarial networks
Internet of Things
link-configuration
modulation and coding scheme
Narrowband
NB-IoT
Performance enhancement
physical resource block
Radio signals
repetitions
Resource management
Signal quality
Signal strength
Training
Uplink
ISSN:2332-7731, 2332-7731
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Zusammenfassung:To enhance the coverage and transmission reliability, repetitions adopted by Narrowband Internet of Things (NB-IoT) allow repeating transmissions several times. However, this results in a waste of radio resources when the signal strength is high. In addition, in low signal quality, the selection of a higher modulation and coding scheme (MCS) level leads to a huge packet loss in the network. Moreover, the number of physical resource blocks (PRBs) per-user needs to be chosen dynamically, such that the utilization of radio resources can be improved on per-user basis. Therefore, in NB-IoT systems, dynamic adaptation of repetitions, MCS, and radio resources, known as auto link-configuration , is crucial. Accordingly, in this paper, we propose SmartCon which is a Generative Adversarial Network (GAN) -based deep learning approach for auto link-configuration during uplink or downlink scheduling, such that the packet loss rate is significantly reduced in NB-IoT networks. For the training purpose of the GAN, we use a Multi-Armed Bandit (MAB) -based reinforcement learning mechanism that intelligently tunes its output depending on the present network condition. The performance of SmartCon is thoroughly evaluated through simulations where it is shown to significantly improve the performance of NB-IoT systems compared to baseline schemes.
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ISSN:2332-7731
2332-7731
DOI:10.1109/TCCN.2021.3130985