CPE Ingress Noise Localization Using Machine Learning
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Solution Overview
Problem
Existing CPE networks face inefficiencies due to ingress noise, which is difficult to identify and localize, leading to high operational costs, downtime, and customer dissatisfaction, especially as networks expand and more devices are added.
Innovation Solution
A system utilizing a machine learning engine to collect and analyze data from CPE devices, detect nodes generating excessive ingress noise, and reduce noise generation by identifying and addressing the sources through intelligent signal management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual troubleshooting methods are used to identify ingress noise sources, then technicians can detect and resolve network issues, but the process is time-consuming, expensive, and requires visiting multiple sites
Solution Approach 1:
The patent replaces manual mechanical troubleshooting processes with an automated electronic system that uses machine learning algorithms to analyze network data and identify ingress noise sources. The system automatically collects data from CPE devices, processes it through trained machine learning models, and generates identification of problem locations without requiring physical site visits.
Solution Approach 2:
The system enables self-service by automatically detecting and localizing ingress noise sources without human intervention. The machine learning engine autonomously analyzes network data, identifies problematic devices or locations, and provides actionable insights, eliminating the need for technicians to manually troubleshoot each issue.
2Reliability
If technicians visit multiple sites to check noise and fix problems, then ingress issues can be resolved, but operational costs increase significantly
Solution Approach 1:
The patent replaces expensive manual field operations with an automated electronic analysis system. The machine learning engine processes network data remotely to identify ingress sources, eliminating the need for technician travel and on-site investigations, thereby significantly reducing operational costs while maintaining effective problem resolution.
Solution Approach 2:
The system introduces an intermediary machine learning analysis layer between network monitoring and field intervention. This intermediary automatically processes network data, identifies problems, and prioritizes interventions, reducing unnecessary site visits and optimizing the deployment of technical resources.
3Quantity of substance
If traditional methods identify hundreds of potential devices causing ingress noise, then comprehensive coverage is achieved, but the ability to locate actual problem sources fails
Solution Approach 1:
The patent changes the analytical parameters by using machine learning models trained on network data patterns to identify ingress sources. Instead of uniformly checking all devices, the system analyzes multiple parameters (signal characteristics, device behavior, network topology) to precisely pinpoint actual problem sources among hundreds of devices.
Solution Approach 2:
The system replaces manual inspection processes with automated electronic analysis using machine learning. The algorithm processes network data to distinguish actual ingress sources from normal devices, achieving high identification accuracy without requiring comprehensive physical inspection of all devices.
4Adaptability or versatility
If networks expand and more devices are added, then service coverage increases, but ingress noise problems worsen
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning system continuously monitors network data, detects ingress noise patterns, and identifies affected areas. This feedback loop enables proactive detection and resolution of ingress issues as they emerge in expanding networks, preventing widespread degradation.
Solution Approach 2:
The system replaces manual network monitoring and manual ingress detection with automated electronic analysis. As networks expand, the machine learning engine automatically adapts to new devices and patterns, continuously identifying ingress sources without requiring proportional increases in manual monitoring resources.
Data Source
AI summary
A method, system, and computer readable storage device detect and localize ingress noise within a customer premises equipment network. Data associated with customer premises equipment activities within a customer premises equipment network is collected. A machine learning engine is trained how to detect ingress data being generated within customer premises equipment nodes of the customer premises equipment network. The machine learning engine then detects customer premises equipment nodes that are generating ingress noise data above a first predetermined level. The machine learning engine then identifies locations of ingress data generation within the detected nodes and further identifies those identified ingress data generation locations that are generating ingress data exceeding a second predetermined level. The method, system, and computer readable storage device can further reduce ingress data generation from the detected identified locations.