By Diana Linton, Charter Communications; Esteban Sandino, Charter Communications; Nader Foroughi, Technetix Inc.; Keith Auzenne, Charter Communications; Premton Bogaj, Technetix Inc.
The ever-increasing demand for high-speed internet connectivity has led to the continuous evolution of Hybrid Fiber-Coaxial (HFC) access networks. To meet these demands efficiently, the adoption of data-driven design and operational approaches has become crucial. This paper delves into the benefits of data-driven design in HFC access networks, outlining how it can lead to improved efficiencies. Through the integration of advanced analytics, predictive modeling and optimization techniques, data-driven design empowers network planners and operators to make informed decisions, allocate resources more efficiently, and enhance network performance, which can lead to additional benefits including: reducing HFC networks’ operational costs and their environmental impact through improved energy efficiency; extending the lifespan of network components, enhancing their reliability, and reducing the need for maintenance; improved signal quality and performance for end-users; and creating a more harmonious coexistence with adjacent frequency bands and other networks and services, enhancing overall network stability and customer satisfaction. In the era of digital transformation, access networks play a pivotal role in delivering high-speed internet services to end-users. HFC networks, a combination of fiber and coaxial cable technologies, have long been a cornerstone of broadband infrastructure. However, the growing demand for bandwidth-intensive applications and the advent of new technologies necessitate innovative approaches particularly for network design. Data-driven design, which involves utilizing data analytics and modeling techniques to guide decision-making, emerges as a powerful tool to optimize HFC access networks. The paper begins with an overview of traditional HFC architecture. Then we discuss key factors that influence Radio Frequency (RF) level optimization, including signal quality, noise, power levels and modulation schemes. Next, we explore challenges related to RF level optimization. Following that, we present the benefits of implementing Artificial Intelligence and/or Machine Learning (AI/ML) tools. Here we examine ways to leverage Cable Modem (CM) data to drive AI/ML tools to optimize HFC network design, performance, and efficiency. We will explore using CM data to dynamically optimize amplifier gain, output level, and performance while staying well above amplifier thermal noise floors and avoiding operation within the amplifier non-linear distortion region. Then we explore the use of a data-driven approach using machine learning techniques for identifying RF signal issues and developing automated solutions for the detection and resolution of signal-impacting events. We conclude with an analysis of signal data collected from approximately 14,000 CMs. The analysis examines the range of RF transmit and receive levels across all CMs to determine available operating margins for a given desired performance. We will also explore how CM RF operating margins can be used to help set the appropriate RF gain for outside plant amplifiers. This approach will seek to take advantage of the smart amplifier capabilities expected to be part of the next generation of 1.8 GHz amplifiers. Optimizing amplifier gain to better reflect actual network conditions may reduce the maximum gain requirements for individual amplifiers and lead to more optimal RF designs and deployments.