ArticleSensors (Basel, Switzerland)2020
Path Loss Prediction based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network and Gaussian Process.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Path Loss Prediction Model of 5G Signal Based on Fusing Data and XGBoost-SHAP Method.Sensors (Basel, Switzerland) · 2025Article
- Sensor-Driven RSSI Prediction via Adaptive Machine Learning and Environmental Sensing.Sensors (Basel, Switzerland) · 2025Article
- A Machine Learning Approach for Path Loss Prediction Using Combination of Regression and Classification Models.Sensors (Basel, Switzerland) · 2024Article
- A Critical Review of the Propagation Models Employed in LoRa Systems.Sensors (Basel, Switzerland) · 2024Review
- Smart Sensors and Smart Data for Precision Agriculture: A Review.Sensors (Basel, Switzerland) · 2024Review
- Application of Artificial Neural Networks for Prediction of Received Signal Strength Indication and Signal-to-Noise Ratio in Amazonian Wooded Environments.Sensors (Basel, Switzerland) · 2024Article
- Enhanced prediction of bolt support drilling pressure using optimized Gaussian process regression.Scientific reports · 2024Article
- Article
- Path loss modeling based on neural networks and ensemble method for future wireless networks.Heliyon · 2023Article
- Deep Learning for Path Loss Prediction at 7 GHz in Urban Environment.IEEE access : practical innovations, open solutions · 2023Article
- Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data.Sensors (Basel, Switzerland) · 2022Article
- Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels.Sensors (Basel, Switzerland) · 2022Article
- Use of Machine Learning for the Estimation of Down- and Up-Link Field Exposure in Multi-Source Indoor WiFi Scenarios.Bioelectromagnetics · 2021Article
- A Deep Neural Network-Based Multi-Frequency Path Loss Prediction Model from 0.8 GHz to 70 GHz.Sensors (Basel, Switzerland) · 2021Article
- Model-Based Adaptive Machine Learning Approach in Concrete Mix Design.Materials (Basel, Switzerland) · 2021Article
- Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network.Materials (Basel, Switzerland) · 2020Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although various linear log-distance path loss models have been developed for wireless sensor networks, advanced models are required to more accurately and flexibly represent the path loss for complex environments. This paper proposes a machine learning framework for modeling path loss using a combination of three key techniques: artificial neural network (ANN)-based multi-dimensional regression, Gaussian process-based variance analysis, and principle component analysis (PCA)-aided feature selection. In general, the measured path loss dataset comprises multiple features such as distance, antenna height, etc. First, PCA is adopted to reduce the number of features of the dataset and simplify the learning model accordingly. ANN then learns the path loss structure from the dataset with reduced dimension, and Gaussian process learns the shadowing effect. Path loss data measured in a suburban area in Korea are employed. We observe that the proposed combined path loss and shadowing model is more accurate and flexible compared to the conventional linear path loss plus log-normal shadowing model.
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Registered trials
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