Dynamic Spectrum Prioritization Using Signal Detection and Geolocation
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Solution Overview
Problem
Existing spectrum management systems struggle with efficiently managing the finite electromagnetic spectrum due to diverse wireless devices and technologies, leading to inefficiencies and interference, especially with the increasing demand for spectrum usage across different regulatory environments.
Innovation Solution
A system for dynamic, prioritized spectrum utilization management that includes monitoring sensors, data analysis engines, and a tip and cue server to detect, learn, and geolocate signals, providing actionable data for optimizing spectrum use and minimizing interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional spectrum management systems are used to regulate radio frequencies, then spectrum usage can be managed over a long period, but spectrum utilization efficiency deteriorates due to finite spectrum resources and exponentially growing wireless technology demands
Solution Approach 1:
The patent implements dynamic spectrum management through machine learning models that continuously adapt spectrum allocation decisions based on real-time environmental data, device requirements, and usage patterns. The system transitions from static, pre-configured spectrum management to dynamic, adaptive decision-making that optimizes spectrum utilization efficiency while responding to changing demands.
Solution Approach 2:
The system changes key parameters including spectrum band selection, power levels, modulation schemes, and allocation timing based on learned patterns from historical and real-time data. These parameter adjustments enable the system to maximize spectrum utilization by selecting optimal configurations for different operational contexts and device requirements.
2Adaptability or versatility
If diverse wireless devices and technologies are supported in the spectrum management system, then adaptability improves, but system complexity increases due to multiple devices, frequencies, and regulatory environments
Solution Approach 1:
The patent creates a universal spectrum management platform that handles multiple wireless device types, frequency bands, and regulatory frameworks through a single integrated system. The machine learning model is designed to process diverse inputs from various device categories (IoT, mobile, fixed wireless) and apply appropriate management policies automatically, eliminating the need for separate management systems for different device types.
Solution Approach 2:
The system introduces an intermediary layer consisting of sensors, data collectors, and machine learning processors that mediate between diverse wireless devices and the core management functions. This intermediary architecture abstracts the complexity of handling multiple device types and regulatory environments, allowing the system to support diverse devices while maintaining manageable operational complexity through standardized interfaces and automated decision-making.
3Ease of operation
If real-time spectrum monitoring and analysis are implemented, then spectrum management flexibility improves, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing spectrum data, device information, and regulatory parameters before real-time decisions are required. Machine learning models are trained offline on historical data to establish patterns and decision rules, enabling rapid real-time spectrum allocation without extensive computational processing during critical decision moments.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors spectrum usage outcomes and device performance, then uses this feedback to refine future spectrum allocation decisions. The machine learning models learn from past decisions and their consequences, progressively improving spectrum management flexibility and efficiency while reducing the computational burden on real-time processing through pattern recognition and adaptive optimization.
Data Source
AI summary
Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.