ML Algorithm Performance to Classify MCS Schemes During UACN Link Adaptation

This research classifies the modulation and coding rate for link adaptation in Underwater Acoustic Communications Networks (UACNs). Recently, the UACN has become a promising technology for military, commercial, and civilian applications, as well as scientific research. However, we should minimize the dataset dimension for real-time implementation due to the sensor nodes’ energy limitations in the underwater environment. We used an Incheon sea trial’s measured dataset of 18 features, applying Principal Component Analysis (PCA) to select the dominant eigenvalue components in order to reduce the curse of dimensionality, and then selected 11 parameters. After that, we applied Machine Learning (ML) algorithms with different combinations of the parameters to separately classify the modulation and the coding rate and measured both individual and overall classification accuracy. The findings are compared with two Taean sea trial datasets with 11 features to finalize the selected parameters for link adaptation. For modulation classification, we observed 96.83% accuracy with the K-nearest Neighbors (KNN) algorithm in three-parameter and two-parameter cases. In coding rate classification, we found 100% accuracy with the KNN algorithm using the same three-parameter case. However, for the best fit among the three datasets, we finalized another three parameters at the expense of accuracy. To find the optimum threshold values for all modulation and coding rate labels, we used Rule-based (RB) 2D and 3D analysis. However, with a hard limit on non-overlapping data, at best, 35.51% classification accuracy was found for a 1/3 coding rate (Turbo code) with QPSK modulation, which showed much less reliability for RB analysis in a UACN, so it is not useful in this regard. Besides, our analysis shows data independence in the
Doppler Spread (DS)
and the
Frequency Shift (FS)
, mitigating the time-variability channel’s challenge. We use the Gaussian distribution plot, a confusion matrix, multi-dimensional scatter plots, interpolated plots to analyze the data.