Enhancing Impairment Detection in Full-Band Capture (FBC) Downstream Spectrum Data Using Unsupervised Learning Methods

Ensuring the reliability of telecommunication networks is paramount in today's interconnected world. To address this critical challenge, this study introduces a comprehensive preprocessing framework tailored for the full-band capture (FBC) downstream spectrum data for impairment detection. The proposed approach initiates with the identification of optimal clustering algorithms and cluster configurations, followed by within-cluster outlier detection which is a crucial preprocessing step. Subsequently, the study delves into amplitude groupings, aimed at elevating impairments detection accuracy. Experimental results demonstrate the effectiveness of this approach, showcasing improved data quality and highlighting the adaptability required in preprocessing pipelines, given the variability in algorithm and cluster count selection across diverse datasets. This research represents a significant step forward in advancing network maintenance, enhancing impairment detection tasks by providing robust preprocessing techniques for diverse FBC downstream spectrum datasets.