首页|Recent Research from Department of Electronics and Telecommunication Engineering Highlight Findings in Machine Learning (Ultrahigh Frequency Path Loss Predictio n Based On K-nearest Neighbors)
Recent Research from Department of Electronics and Telecommunication Engineering Highlight Findings in Machine Learning (Ultrahigh Frequency Path Loss Predictio n Based On K-nearest Neighbors)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting from Maharashtra, India, by NewsRx journalists, research stated, "Path loss prediction (PLP) is an importan t feature of wireless communications because it allows a receiver to anticipate the signal strength that will be received from a transmitter at a given distance . The PLP is done by using machine learning models that take into account numero us aspects such as the frequency of the signal, the surroundings, and the type o f antenna." The news correspondents obtained a quote from the research from the Department o f Electronics and Telecommunication Engineering, "Various machine learning metho ds are used to anticipate path loss propagation but it is difficult to predict p ath loss in unknown propagation conditions. In existing models rely on incomplet e or outdated data, which can affect the accuracy and reliability of predictions and they do not take into account the effects of environmental factors, such as terrain, foliage, and weather conditions, on path loss. Furthermore, existing m odels are not robust enough to handle the real-world variability and uncertainty , leading to significant errors in predictions. To tackle this issue, a novel ul trahigh frequency (UHF) PLP based on K-nearest neighbors (KNNs) is developed for predicting and optimizing the path loss for UHF. In this proposed model, a KNN- based PLP has been used to predict the path loss in the UHF. This technique is u sed for high-accuracy PLP through KNN forecast route loss by determining the K-n earest data points to a particular test point based on a distance metric. Moreov er, the existing models were not able to optimize path loss due to complex and l arge-scale machine learning models. Therefore, the stochastic gradient descent t echnique has been used to minimize the objective function, which is often a meas ure of the difference between the model's predictions and the actual output that will fine-tune the parameters of the KNN model, by measuring the similarity bet ween data points."
MaharashtraIndiaAsiaCyborgsEmerg ing TechnologiesK-nearest NeighborMachine LearningDepartment of Electronic s and Telecommunication Engineering
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