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

In light of the technological advancements that require faster data speeds, there has been an increasing demand for higher frequency bands. Consequently, numerous path loss prediction models have been developed for 5G and beyond communication networks, particularly in the millimeter-wave and subterahertz frequency ranges. Despite these efforts, there is a pressing need for more sophisticated models that offer greater flexibility and accuracy, particularly in challenging environments.

Performance analysis of improved path loss models for millimeter-wave wireless network channels at 28 GHz and 38 GHz.

The importance of the path loss in millimeter wave channel propagation cannot be taken for granted in terms of deployment, design, performance assessment, and planning. The path loss helps to determine the network's geographic coverage. Although many path loss models, including statistical and empirical models based on measurement and linear regression, have been proposed by various researchers, high fidelity is required to determine the performance of the wireless network's channel.

Path Loss Measurements and Model Analysis in an Indoor Corridor Environment at 28 GHz and 38 GHz.

This paper examines the large-scale path loss models for an indoor corridor environment at frequencies of 28 and 38 GHz. The measurement environment consists of an indoor corridor with both line-of-sight (LOS) and non-line of sight (NLOS) scenarios using vertical-vertical (V-V) and vertical-horizontal (V-H) antenna polarizations. The single-frequency close-in (CI), floating intercept (FI), free space large-scale path loss models and measured data from the measurement campaign were used to evaluate the performance analysis.

Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels.

Unlimited access to information and data sharing wherever and at any time for anyone and anything is a fundamental component of fifth-generation (5G) wireless communication and beyond. Therefore, it has become inevitable to exploit the super-high frequency (SHF) and millimeter-wave (mmWave) frequency bands for future wireless networks due to their attractive ability to provide extremely high data rates because of the availability of vast amounts of bandwidth. However, due to the characteristics and sensitivity of wireless signals to the propagation effects in these frequency bands, more accurate path loss prediction models are vital for the planning, evaluating, and optimizing future wireless communication networks.