From Manual to Automated: AI-Driven Network Engineering and Operations (2023)

By Nader Foroughi, Technetix Inc.; Chris Beem, Technetix Inc.; Diego Royo Moros, Technetix Inc.

The hybrid fiber-coax (HFC) networks are rapidly changing as we approach mass deployments of next generation DOCSIS and spectrum expansion. Knowing that HFC networks amalgamate the strengths of optical fiber and coaxial cable technologies, we are faced with a new level of intricacy to their design and maintenance. As the frequencies utilized in these networks continue to climb, the intricacies associated with HFC networks have surged significantly. One of the key factors contributing to this intricacy is the complex interdependencies among the various departments engaged in the design and maintenance process of HFC networks. The alignment of different segments such as network planning, engineering, and operations has become pivotal for the seamless functioning of these networks. As the demand for higher data speeds and bandwidth increases, the frequency spectrum used in HFC networks expands, leading to enhanced complexity in design. With higher frequencies, challenges such as signal attenuation, interference, and noise become more pronounced. Furthermore, different departments—such as engineering, operations, and maintenance—become increasingly interdependent due to the intricate nature of these networks. Ensuring efficient signal transmission, maintaining signal quality, and minimizing signal degradation are tasks that require a meticulous approach. This paper delves into how network automation and software defined networks can alleviate a large majority of the concerns, both from plant upgrade and on-going maintenance perspectives. A central focus of this exploration is the transformative potential of artificial intelligence (AI) in reshaping the landscape of end-to-end design and maintenance within the realm of HFC networks. By harnessing the power of AI, it becomes possible to reimagine and revolutionize the conventional methodologies that govern the optimization of nodes, amplifiers, and the overall capacity management. The utilization of AI-driven algorithms and predictive analytics offers a promising avenue for addressing the challenges associated with HFC networks, paving the way for enhanced efficiency, reliability, and adaptability. This paper interlaces the potential advantages of AI in HFC network design with its impacts across the diverse realm of network management. These benefits span from substantial cost savings via power and resource optimization to informed decision-making enabled by data-driven AI insights. Moreover, the infusion of AI can also increase the plant reliability of HFC networks, enhancing proactive maintenance and anomaly detection algorithms.

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