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

¹ Department of Electrical and Electronics Engineering, Academy of Maritime Education and Training (AMET) Deemed University, East Coast Road, Kanathur, Chennai
² Department of ECE, Sri Venkateshwara College of Engineering, Bengaluru - 562157
³ Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore
⁴ Department of Electrical and Electronics Engineering, Sri Eshwar College of Engineering, Coimbatore.
⁵ Department of ECE, Maha Barathi Engineering College, Vasudevanur, Chinnasalem, Kallakurichi (Dt), Tamil Nadu - 606201.
⁶ Department of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu – 602105, India.

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Published online: 26 June 2026

Abstract

The mmWave frequency band is crucial for supporting the high data rate requirements of 6G. However, their characteristics of transmission are very sensitive to several factors like the distance between antennas, barriers, materials, and environmental conditions. Therefore, it is necessary to accurately model the transmission characteristics for reliable deployments. In this paper, we have reviewed many of the most important mmWave propagation models that include Friis, COST-231, NYUSIM, 3GPP TR 38.901, Ray Tracing, and Hybrid Machine Learning Techniques. The performance of each of the above-mentioned models with regard to Path Loss, RSS, Delay Spread, Blockage Impact, and Angular Dispersions for both Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) were evaluated. We have included practical model graphs to help compare and select different models for use in different environments. We have also explored the coverage enhancement techniques used in RIS, Beamforming, and AI based Prediction. Finally, we have identified the main challenges, cross-environment generalization, and outlined future research directions for 6G Network Planning and Optimization.