Wireless Site Interference Pattern Detection Using Signal Vectors

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Solution Overview

Problem

Existing wireless communication systems face challenges in accurately identifying and mitigating interference patterns, leading to degraded performance and capacity due to manual inefficiencies and time-consuming processes in tracking and correcting interference sources.

Innovation Solution

A system that utilizes signal strength measurements to identify interference patterns, represented as vectors, and groups wireless communication sites based on these patterns using machine learning algorithms, enabling automatic identification and mitigation of interference sources.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If manual methods are used to identify and track interference sources, then the process is simple to implement, but the speed and efficiency of interference pattern identification are degraded

Engineering Contradiction:

Improvespeed of interference pattern identificationVSAvoidcomplexity of identification system

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical identification processes with automated electronic systems that use signal strength measurements and machine learning algorithms to identify interference patterns, thereby increasing speed while accepting increased system complexity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables automatic identification and mitigation of interference sources through self-learning machine learning models that continuously improve their ability to detect and classify interference patterns without manual intervention

Inventive Principle:

Principle #25Self-service

2Productivity

If manual processes are used for interference mitigation, then the system is easier to operate, but the productivity and efficiency are reduced

Engineering Contradiction:

Improveefficiency of interference mitigationVSAvoidease of system operation

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The machine learning system performs automatic interference pattern recognition and mitigation without requiring manual operation, thereby significantly improving productivity while reducing the need for operator intervention

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously monitors signal strength measurements and uses the results to refine its interference identification accuracy over time, creating a self-improving system that increases productivity through automated feedback loops

Inventive Principle:

Principle #23Feedback

3Measurement precision

If traditional methods are used to track interference sources, then the process is simpler, but the accuracy of interference pattern identification is degraded

Engineering Contradiction:

Improveaccuracy of interference pattern identificationVSAvoidtime required for identification process

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary signal strength measurements and pattern recognition in advance, allowing for rapid and accurate interference identification when needed, thereby improving both accuracy and reducing time loss

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Traditional manual tracking methods are replaced with automated electronic measurement and machine learning-based pattern recognition systems that simultaneously improve accuracy while reducing the time required for identification

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12432767B2System and method for interference pattern identification

Publication Date: 2025.09.30 VERIZON PATENT & LICENSING INC

AI summary

One or more computing devices, systems, and/or methods for identifying interference patterns associated with wireless communication sites and/or grouping wireless communication sites based upon identified interference patterns are provided. In an example, signal strength information associated with a wireless communication site communicating with a user equipment (UE) may be determined. An interference pattern representation of one or more interference patterns associated with the wireless communication site may be generated based upon the signal strength information. A plurality of wireless communication sites, including the wireless communication site, may be grouped into groups of wireless communication sites based upon interference pattern representations associated with the plurality of wireless communication sites. The interference pattern representations include the interference pattern representation.