Unsupervised Learning for Detection of Leakage from the HFC Network

In the context of proactive maintenance of the HFC networks, cable operators count on Full-Band Capture (FBC) to analyze the downstream spectrum and look for impairments. There exists one particular type of impairment, which is ingress, likely to happen along with leakage. Therefore, the detection of the former leads to the identification of the latter. We collect data from FBC tool, and use unsupervised machine learning to group cable modems such that the signal they receive show common patterns. This allows a characterization of all cable modems in a service group. Then, we use the modems' locations to determine whether the root cause of the flaw is inside the homes or not.