Performance Analysis of Fixed Broadband Wireless Access in mmWave Band in 5G
This work addresses the cost and deployment challenges of fiber access for operators in areas lacking infrastructure, though it is incremental as it builds on existing datasets with real-world data and machine learning methods.
The study tackled the problem of deploying high-speed fixed broadband wireless access in mmWave bands for 5G by analyzing real-world transmission data to enable self-configuration, achieving up to 99% accuracy in detecting transmission angle and distance.
An end-to-end fiber-based network holds the potential to provide multi-gigabit fixed access to end-users. However, deploying fiber access, especially in areas where fiber is non-existent, can be time-consuming and costly, resulting in delayed returns for Operators. This work investigates transmission data from fixed broadband wireless access in the mmWave band in 5G. Given the growing interest in this domain, understanding the transmission characteristics of the data becomes crucial. While existing datasets for the mmWave band are available, they are often generated from simulated environments. In this study, we introduce a dataset compiled from real-world transmission data collected from the Fixed Broadband Wireless Access in mmWave Band device (RWM6050). The aim is to facilitate self-configuration based on transmission characteristics. To achieve this, we propose an online machine learning-based approach for real-time training and classification of transmission characteristics. Additionally, we present two advanced temporal models for more accurate classifications. Our results demonstrate the ability to detect transmission angle and distance directly from the analysis of transmission data with very high accuracy, reaching up to 99% accuracy on the combined classification task. Finally, we outline promising future research directions based on the collected data.