Path loss modeling based on neural networks and ensemble method for future wireless networks

Elmezughi, Mohamed K.; Salih, Omran; Afullo, Thomas J.; Duffy, Kevin J.

doi:10.1016/j.heliyon.2023.e19685

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Path loss modeling based on neural networks and ensemble method for future wireless networks

Cited by 5 publications

References 61 publications

Performance Evaluation of GeoAI-Based Approach for Path Loss Prediction in Cellular Communication Networks

Performance Evaluation of GeoAI-Based Approach for Path Loss Prediction in Cellular Communication Networks

A novel pathloss prediction and optimization approach using deep learning in millimeter wave communication systems

Intelligent Recognition for Electromagnetic Targets

Contact Info

Product

Resources

About