Agenda

AI & Automation

AI-Driven Insights for Outside Plant and Customer Premises

Automating Amplifier Analysis with PNM Full Band Capture, RxMER, for Activation of a Second OFDM Channel      

  • Understanding the operational challenges of deploying additional downstream channels
  • Assessing the impact of total composite power on amplifier performance and linear operation, overall system performance and achievable capacity
  • Reviewing the fundamental theory behind the phenomenon alongside examples obtained from lab measurements
  • Introducing a method for detecting amplifier nonlinearity based on measuring the distortion noise from a cable modem’s full band capture (FBC)
  • Performing data analysis across the entire vCMTS footprint to inform deployment priority
  • Developing a statistical model for the prediction of amplifier non-linearity

Maher Harb
Distinguished Engineer
Comcast

You Might Have a Screw Loose: Remote Detection of Thermal Imperfections

  • Developing robust monitoring systems to detect thermal problems through a range of operating temperatures
  • Using multiple sensors to remotely detect the thermal health of  node and identify fixable problems before they impact performance
  • Combining field studies and real-world trials together to turn remote sensor readings into actionable network intelligence

Matt Wichman
Director Network Operations
Comcast

Proactive Network Maintenance for Customer Premise Issues in DOCSIS® Network 

  • Developing a framework for efficient implementation of proactive network maintenance (PNM) when DOCSIS 4.0 is implemented at scale
  • Utilizing advanced predictive frameworks and deep learning models to enable timely identification and resolution of customer premise issues in DOCSIS networks
  • Leveraging data from a variety of sources to create holistic insights and address potential faults before they impact service quality

Vikram Karwal
Senior Data Scientist
Rogers Communications

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