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Predictive Wireless Channel Modeling of MmWave Bands Using Machine Learning

https://doi.org/10.3390/electronics10243114

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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The Elements of Statistical Learning

inferredcites →https://doi.org/10.1109/access.2018.2837692openalex-snapshot

Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks

inferredcites →https://doi.org/10.1109/access.2019.2909490openalex-snapshot

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

inferredcites →https://doi.org/10.1109/access.2019.2931072openalex-snapshot

Path Loss Exponent and Shadowing Factor Prediction From Satellite Images Using Deep Learning

inferredcites →https://doi.org/10.1109/access.2019.2960614openalex-snapshot

An Indoor Channel Model for High Data-Rate Communications in D-Band

inferredcites →https://doi.org/10.1109/cc.2017.8233654openalex-snapshot

Deep learning for wireless physical layer: Opportunities and challenges

inferredcites →https://doi.org/10.1109/comst.2020.2965856openalex-snapshot

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

inferredcites →https://doi.org/10.1109/glocom.2017.8254052openalex-snapshot

Machine Learning-Based Channel Classification and Its Application to IEEE 802.11ad Communications

inferredcites →https://doi.org/10.1109/glocom.2018.8647921openalex-snapshot

Propagation Measurement System and Approach at 140 GHz-Moving to 6G and Above 100 GHz

inferredcites →https://doi.org/10.1109/jrproc.1946.234568openalex-snapshot

A Note on a Simple Transmission Formula

inferredcites →https://doi.org/10.1109/mwc.2016.1500356wcopenalex-snapshot

Machine Learning Paradigms for Next-Generation Wireless Networks

inferredcites →https://doi.org/10.1109/tap.2017.2705112openalex-snapshot

A UHF Path Loss Model Using Learning Machine for Heterogeneous Networks

inferredcites →https://doi.org/10.1109/tvt.2016.2543139openalex-snapshot

Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications

inferredcites →https://doi.org/10.1109/twc.2018.2828001openalex-snapshot

Outdoor Wideband Channel Measurements and Modeling in the 3–18 GHz Band

inferredcites →https://doi.org/10.1109/vtcspring.2016.7504435openalex-snapshot

Propagation Path Loss Models for 5G Urban Micro- and Macro-Cellular Scenarios

inferredcites →https://doi.org/10.1109/wpmc48795.2019.9096057openalex-snapshot

Predicting the Path Loss of Wireless Channel Models Using Machine Learning Techniques in MmWave Urban Communications

inferredcites →https://doi.org/10.1109/wpmc48795.2019.9096156openalex-snapshot

Relay Selection for 5G New Radio Via Artificial Neural Networks

inferredcites →https://doi.org/10.1145/3337065openalex-snapshot

Big Data Analytics for Large-scale Wireless Networks

inferredcites →https://doi.org/10.1155/2018/8489326openalex-snapshot

Air‐to‐Air Path Loss Prediction Based on Machine Learning Methods in Urban Environments

inferredcites →https://doi.org/10.1186/s13638-019-1424-2openalex-snapshot

EKF/UKF-based channel estimation for robust and reliable communications in V2V and IIoT

inferredcites →https://doi.org/10.3390/app9091908openalex-snapshot

Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion

inferredcites →https://doi.org/10.3390/electronics8030286openalex-snapshot

An Efficient 3-D Ray Tracing Method: Prediction of Indoor Radio Propagation at 28 GHz in 5G Network

inferredcites →https://doi.org/10.3390/s19163541openalex-snapshot

A Real-Time Channel Prediction Model Based on Neural Networks for Dedicated Short-Range Communications

inferredcites →https://doi.org/10.5194/gmd-7-1247-2014openalex-snapshot

Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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An Empirical Study on Using CNNs for Fast Radio Signal Prediction

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An overview of 6G wireless networks

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Comprehensive Survey of mmWave Propagation Models and their Applications in 6G Wireless Networks

inferred← citeshttps://doi.org/10.1109/icetci67340.2025.11258025openalex-snapshot

Machine Learning-Based Path Loss Models: Towards a Unified Methodology

inferred← citeshttps://doi.org/10.1109/icrcicn68210.2025.11364846openalex-snapshot

Real Time Prediction of Signal Strength for 4G and 5G Communication

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Physics-Guided Machine Learning for Path Loss Prediction: A Review

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Multi-Agent DDPG Based Resource Allocation in NOMA-Enabled Satellite IoT

inferred← citeshttps://doi.org/10.1155/2022/1864290openalex-snapshot

Cooperative Communications Based on Deep Learning Using a Recurrent Neural Network in Wireless Communication Networks

inferred← citeshttps://doi.org/10.18196/jrc.v3i4.15539openalex-snapshot

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

inferred← citeshttps://doi.org/10.3390/app12157685openalex-snapshot

Improving Path Loss Prediction Using Environmental Feature Extraction from Satellite Images: Hand-Crafted vs. Convolutional Neural Network

inferred← citeshttps://doi.org/10.3390/electronics12030497openalex-snapshot

Predicting Path Loss of an Indoor Environment Using Artificial Intelligence in the 28-GHz Band

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Blockage Prediction of an Urban Wireless Channel Characterization Using Classification Artificial Intelligence

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