Cognitive Radio-based Context-Aware Link Adaptation for Coverage Extension of Narrow Band Internet of Things

Cognitive Radio-based Context-Aware Link Adaptation for Coverage Extension of Narrow Band Internet of Things

© 2022 by IJETT Journal
Volume-70 Issue-12
Year of Publication : 2022
Author : V. Nallarasan, Kottilingam Kottursamy
DOI : 10.14445/22315381/IJETT-V70I12P212

How to Cite?

V. Nallarasan, Kottilingam Kottursamy, "Cognitive Radio-based Context-Aware Link Adaptation for Coverage Extension of Narrow Band Internet of Things," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 109-117, 2022. Crossref,

Coverage extension with limited transmission power devices is one of the requirements and research challenges for battery-operated IoT nodes, which use a narrowband IoT wireless communication protocol. The link adaptation mechanism can solve this problem by selecting optimal parameters using cognitive radio. This research work proposes a context-aware link adaptation mechanism using a cognitive radio that uses a machine-learning algorithm. The proposed mechanism achieves greater coverage in the long run with lower SINR (signal-to-interference and noise ratio) and BER (bit error rate) through optimal selection of repetition rate, modulation, coding scheme, transmission power, number of subcarriers, and frequency based on the wireless channel condition and QoS requirement of the application. Here, every Narrowband Internet of Things (NBIoT) node is considered a cognitive radio node, which uses a frequency that is available for free. The proposed system-generated NBIoT uplink waveform and evaluated the performance using the optimal parameter derived from the proposed context-aware machine learning-based link adaptation scheme.

Cognitive Radio, Link adaptation, Narrowband internet of things, SVM Regression, Decision tree Regression, Internet of things.

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