The Proactive and Reactive Power of DOCSIS PNM Tools
By Brady Volpe
In the demanding realm of DOCSIS networks, being proactive has become a necessity, not a luxury. To ensure efficient operation of these networks, cable operators must embrace proactive network maintenance (PNM) tools. These resources not only offer the ability to anticipate and address network issues before they significantly affect service delivery, thus creating a more robust and reliable network for users, but they can also be utilized reactively to help optimize the workforce, improve subscriber quality of experience (QoE) and are not impacted by supply chain constraints. Let’s explore how.
Proactive network maintenance—reactive and proactive
PNM consists of a suite of tools designed to predict and mitigate network issues. By analyzing data from the DOCSIS network, cable operators can spot early signs of network problems and intervene before they impact performance or lead to downtime. PNM tools have evolved to become increasingly sophisticated, providing in-depth insights into network health and performance. While the emphasis on proactivity has been considerable, many cable operators have discovered that these tools are equally effective as reactive maintenance tools. They highlight impairments that traditional tools often overlook, such as micro-reflections and group delay.
Understanding PNM tools in DOCSIS networks
PNM tools deliver a wealth of functionalities that support the maintenance of DOCSIS networks. With capabilities ranging from DOCSIS pre-equalization analysis to full band capture spectrum analyzers, along with plant map overlays for outage detection, these resources furnish operators with real-time insights into the status of their networks. Utilizing these tools enables cable operators to identify potential issues, including micro-reflections, in-home wiring problems, and other plant impairments, which could undermine network performance.
Furthermore, upstream triggered spectrum capture (UTSC) leverages dedicated fast Fourier transform (FFT) processing hardware incorporated into either the cable modem termination system (CMTS) or remote-PHY devices (RPDs). This enables return path monitoring that is equivalent to, if not superior to, legacy return path monitoring systems. The advanced capabilities of the CMTS or RPD allow them to ‘trigger’ based on upstream burst noise and even individual modem transmissions. This is possible because UTSC utilizes the CMTS MAC-layer processing, making it cognizant of each device transmitting data to the CMTS or RPD. Furthermore, UTSC provides visibility across all distributed access architecture (DAA) deployments and orthogonal frequency division multiple access (OFDMA) channels, which is crucial for DOCSIS 3.1 and the forthcoming DOCSIS 4.0 deployments.
Figure 1. UTSC-based return path monitoring (shown with six SC-QAM and one OFDMA channel in an 85 MHz upstream plant).
Leveraging PNM for plant maintenance
With the insights provided by PNM tools, operators can more effectively conduct predictive maintenance on their networks. For example, by identifying patterns in network data, operators can foresee issues that may lead to failure. These could be gradual changes in signal quality or sudden increases in packet loss. By addressing these signs proactively, operators can prevent network downtime and ensure continuous, high-quality service delivery.
PNM reactively, really!
From a reactive perspective, cable operators can use PNM tools to resolve subscriber issues more efficiently. A classic scenario involves resource utilization. Suppose a subscriber contacts a customer service representative (CSR) regarding a service problem. In the absence of PNM, the typical response would be to dispatch a technician to the subscriber’s home (see Figure 2).
However, with PNM in play, the cable operator can determine whether the impairment is confined to the subscriber’s home network or resides in the outside plant (OSP). Deploying a technician to a subscriber’s home for an issue originating in the OSP would result in an unnecessary truck roll, incurring avoidable expenses for the operator and causing frustration for the subscriber. In such a scenario, PNM empowers the operator to make a better-informed decision: to dispatch the truck to the specific OSP location where the impairment lies. By addressing the root cause, the operator can potentially resolve the issue for multiple affected subscribers. This not only optimizes resource usage but also prevents the subscriber from enduring an inconvenient wait at home, especially when the problem doesn’t originate from there (see Figure 3).
Use PNM to work smarter, not harder!
Case study
A prime example of utilizing PNM for predictive maintenance comes from a North American cable operator. This operator integrated a DOCSIS PNM toolset into their network maintenance strategy, which led to an impressive result. They detected a stretch of coax cable beneath a multi-lane highway that was degrading, although it wasn’t yet completely obstructing RF signals. Considering the weeks or months required for legal procedures and local regulatory approvals to replace the coax cable under the highway, the early detection by PNM was immensely beneficial. This advanced warning allowed the operator to coordinate with state and city governments in a timely manner, securing necessary approvals to replace the problematic coax before any disruption in service for subscribers.
The outcome was a marked shift in standard operational procedure. Had the coax cable abruptly ceased transmitting RF signals to subscribers without the forewarning from PNM, the cable operator’s customers would have suffered prolonged service disruptions. Moreover, the cable operator would have had to invest a great deal more money and resources in rushing the repair process. As it was, they were able to minimize network downtime and enhance customer satisfaction. This illustrates the concrete benefits of adopting a proactive approach to maintenance.
Challenges and potential solutions
While the potential of PNM tools is immense, their deployment is not without hurdles. These may involve the intricacies of incorporating PNM tools into existing network structures, equipping staff with the skills to utilize these tools effectively, and managing the massive amount of data generated. Nevertheless, with a calculated implementation strategy, complemented by continuous training and support, these challenges can be surmounted. One pivotal element of this strategic approach is securing training and endorsement from senior management.
“Like any tool, PNM works best when it is used and used often.” — Jason Rupe, CableLabs
Conclusion
The significance of utilizing PNM tools for both proactive and reactive maintenance within DOCSIS networks is immeasurable. By preemptively addressing potential network issues, operators can elevate the reliability and performance of their networks, thus enriching the end-user experience. In a reactive capacity, PNM tools enable cable operators to deploy their resources more efficiently, facilitating cost avoidance and enhancing subscriber QoE. As network demands escalate, the necessity for robust, reliable, and efficient network management tools such as PNM will only grow more acute. Our collective challenge within the industry is to persistently refine and evolve these tools, assuring that we remain a step ahead in our network maintenance strategies.
Figure 2. Classic workflow without PNM.
Figure 3. PNM workflow with PNM as a reactive tool.
Brady S. Volpe
brady.volpe@volpefirm.com
Mr. Volpe, the Chief Product Officer at OpenVault, brings over 30 years of experience in the broadband cable and telecommunications industry. As the founder of The Volpe Firm, Inc and Nimble This (now OpenVault), he has been instrumental in product development and successful launches. Through his acclaimed blog, podcast – get your tech on, and livestream, he shares expertise in high-speed data, DOCSIS, PNM, HFC, ML, PMA, and more. Mr Volpe has an unwavering commitment to broadband innovation. Mr. Volpe has a MSEE with honors from The Johns Hopkins University Applied Physics Laboratory and a BSEE from Penn State University.
Images by author / shutterstock
