摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑-每日新闻-关于机器学习的最新研究结果已经发表。根据NewsRx记者在印度马哈拉施特拉邦的新闻报道，研究表明：“路径损耗预测(PLP)是无线通信的一个重要特征，因为它允许接收器预测在给定距离内从发射器接收到的信号强度。PLP是通过使用机器学习模型来完成的，该模型考虑了信号的频率等许多方面，"周围环境和O型天线."新闻记者从电子与通信工程系的研究中得到一句话:“人们使用各种机器学习方法来预测路径损耗的传播,但在未知传播条件下很难预测路径损耗。在现有的模型中,依赖于不完整或过时的数据。”这可能会影响预测的准确性和可靠性，并且它们没有考虑到地形、树叶和天气条件等环境因素对路径损失的影响。此外，现有的模型不足以处理现实世界的变异性和不确定性，导致预测出现重大错误。提出了一种基于k近邻(KNNs)的超高频(UHF)PLP,用于预测和优化UHF信道损耗。本文提出了一种基于KNN的UHF路径损耗预测方法,该方法通过基于距离度量的KNN最优数据点来预测UHF路径损耗,从而实现高精度的路径损耗预测,同时,由于现有的模型复杂且规模大,不能优化路径损耗,因此,本文提出了一种基于KNN的UHF路径损耗预测方法。随机梯度下降技术被用来最小化目标函数，目标函数通常是模型预测和实际输出之间的差异的度量，通过测量数据点之间的相似性来微调KNN模型的参数。
Abstract
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 In existing models rely on incomplet e or outdated data, which can affect the accuracy and reliability of predictions and they do not take into account the effects of environmental factors, such as terrain,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."
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