When informed by vast amounts of network performance information, identifying radio frequency (RF) problems with the Data-Over-Cable Service Interface Specifications (DOCSIS) isn’t that hard. However, determining if the problems are inside or outside the home can be difficult. This is a decades-old problem, with hopes often pinned on the elusive promise of artificial intelligence (AI) or machine learning (ML) to help. The challenge that many data scientists will tell you is that having good training data is critical. The lack of a reliable feedback loop to establish cause-and-effect often results in poorly trained machines.
A significant amount of time and resources has been poured into remote diagnostic tools to identify plant problems. Those tools historically have been segmented, specialized, and tuned to evaluate singular aspects of the RF health – for example, receive modulation error ratio (RxMER) and forward error correction (FEC). Once a problem is identified, determining if its source is in a customer’s home, drop or tap has historically been left to technicians, to provide feedback about what they found. The feedback mechanisms typically involve selecting a code or result and updating the work order when it’s complete.
With the COVID-19 pandemic starting mid-March 2020, the rapid development of an “outside network check” provided an opportunity to gather better features and labels. With a renewed desire to keep technicians and customers isolated, the team is exploring new ML/AI models. These new models are trained to use cloud-based RF measurements. These measurements include remote telemetry from DOCSIS devices, and other equipment logged by collection systems. Another set of measurements is taken at the tap and ground block, finally offering a way to segment the network and train the machines differently. The authors review the outcome of this fascinating exercise currently under way, as this paper is being written, in the summer of 2020.