Machine Learning-Based Adaptive Equalization with Software-Dened Radio Experimental Setup

Abstract

High-speed data transfer over the communication channel is now possible due to developments in wireless communication. However, as these data are transmitted over the channels, multiple elements' interference and interventions will disrupt the network's functionality, frequently causing data to be misinterpreted or distorted owing to overlap. Channel equalization is a notion that can be applied with the help of machine learning and artificial intelligence to counteract this kind of interference. The hybrid technique, which extracts features from an equalizer utilized in the channel in training and tracking modes, is the subject of current research efforts. Machine learning techniques are applied to distinguish between low, high, medium, and open space situations. Analysing the radio frequency signals that travel across the channel allows for distinction. The outcome and examination of multiple machine algorithms show that the suggested model functions well in a real-time setting. Multiple sample ratios and classifier models are used to train and test algorithms such as SVM, decision tree, random forest, KNN, logistic regression, and naive Bayes. Based on the parameters mapped by the confusion matrix, machine learning algorithms' performance and efficiency are estimated. When there are fewer samples overall, the random forest algorithm performs better than other algorithms. When there are more samples, the tree-based approach produces superior results. Decision trees can be implemented in real time since, in comparison, all environment types in high, low, and medium cluttered environments have produced better results.