  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.
