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http://dx.doi.org/10.22156/CS4SMB.2020.10.05.001

Machine Learning-based MCS Prediction Models for Link Adaptation in Underwater Networks  

Byun, JungHun (Department of Computer Science, Chungbuk University)
Jo, Ohyun (Department of Computer Science, Chungbuk University)
Publication Information
Journal of Convergence for Information Technology / v.10, no.5, 2020 , pp. 1-7 More about this Journal
Abstract
This paper proposes a link adaptation method for Underwater Internet of Things (IoT), which reduces power consumption of sensor nodes and improves the throughput of network in underwater IoT network. Adaptive Modulation and Coding (AMC) technique is one of link adaptation methods. AMC uses the strong correlation between Signal Noise Rate (SNR) and Bit Error Rate (BER), but it is difficult to apply in underwater IoT as it is. Therefore, we propose the machine learning based AMC technique for underwater environments. The proposed Modulation Coding and Scheme (MCS) prediction model predicts transmission method to achieve target BER value in underwater channel environment. It is realistically difficult to apply the predicted transmission method in real underwater communication in reality. Thus, this paper uses the high accuracy BER prediction model to measure the performance of MCS prediction model. Consequently, the proposed AMC technique confirmed the applicability of machine learning by increase the probability of communication success.
Keywords
Link adaptation; Adaptive modulation and coding; Classification model; Machine learning; Underwater IoT network; ACM;
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