has been cited by the following article(s):
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[1]
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A Frequency Domain Predictive Channel Model for 6G Wireless MIMO Communications Based on Deep Learning
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IEEE Transactions on …,
2024 |
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[2]
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Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling
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Sensors,
2024 |
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[3]
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Machine learning techniques for received signal strength indicator prediction
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Intelligent Data …,
2023 |
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[4]
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Artificial intelligence enabled radio propagation for communications—part II: scenario identification and channel modeling
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… on Antennas and …,
2022 |
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[5]
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Effect of Training Algorithms and Network Architecture on the Performance of Multi-Band ANN-Based Path Loss Prediction Model
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2022 IEEE Nigeria …,
2022 |
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[6]
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Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis
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2021 |
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[7]
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Feature Engineering for Machine Learning and Deep Learning Assisted Wireless Communication
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2021 |
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[8]
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Application of artificial neural network modeling techniques to signal strength computation
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2021 |
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[9]
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A Model for Wireless Signal Path Loss using Radiosity Technique
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2020 |
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[10]
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Machine‐learning‐based prediction methods for path loss and delay spread in air‐to‐ground millimetre‐wave channels
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… Microwaves, Antennas & …,
2019 |
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[11]
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Machine-learning-based prediction methods for path loss and delay spread in air-to-ground millimetre-wave channels
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2019 |
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[12]
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ANFIS Model for Path Loss Prediction in the GSM and WCDMA Bands in Urban Area
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2019 |
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[13]
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Modelling of propagation path loss using adaptive hybrid artificial neural network approach for outdoor environments.
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ResearchSpace,
2018 |
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[14]
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Machine learning methods for SIR prediction in cellular networks
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Physical Communication,
2018 |
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[15]
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Bayesian Regularization in Multi-Layer Perceptron Artificial Neural Network Model to Predict Signal Power Loss Using Measurement Points
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2018 |
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[16]
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ON ADAPTIVE NEURO-FUZZY MODEL FOR PATH LOSS PREDICTION IN THE VHF BAND
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2017 |
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[17]
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基于云存储服务的 AR 博物馆系统研究
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计算机工程与应用,
2017 |
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[18]
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Adaptive Neuro-Fuzzy model for path loss prediction in the VHF band
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2017 |
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[19]
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Elektromanyetik dalga yayılım modellemesinin (Epstein-Peterson) yapay sinir ağı modeli kullanılarak analiz edilmesi
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Thesis,
2016 |
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[1]
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Artificial Intelligence Enabled Radio Propagation for Communications—Part II: Scenario Identification and Channel Modeling
IEEE Transactions on Antennas and Propagation,
2022
DOI:10.1109/TAP.2022.3149665
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[2]
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Effect of Training Algorithms and Network Architecture on the Performance of Multi-Band ANN-Based Path Loss Prediction Model
2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON),
2022
DOI:10.1109/NIGERCON54645.2022.9803057
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[3]
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Artificial Intelligence Enabled Radio Propagation for Communications—Part II: Scenario Identification and Channel Modeling
IEEE Transactions on Antennas and Propagation,
2022
DOI:10.1109/TAP.2022.3149665
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[4]
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Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis
Mobile Information Systems,
2021
DOI:10.1155/2021/6619364
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[5]
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A Model for Wireless Signal Path Loss using Radiosity Technique
2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC),
2020
DOI:10.1109/IMITEC50163.2020.9334133
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