Electronic Spectrum Management Databases for Real-Time Signal Classification
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Solution Overview
Problem
Existing spectrum management devices are limited by their specificity to narrow frequency ranges, bulkiness, high cost, difficulty in use, and lack of real-time data analysis, making them inefficient for managing diverse wireless communications spectrum needs.
Innovation Solution
A system that automatically identifies and classifies wireless communications signals in near real-time using sensors, processors, and memory to analyze signal characteristics, providing real-time analytics and identifying open spaces for communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If narrowly tailored spectral analyzer devices are used for specific frequency ranges, then measurement precision for that specific range is improved, but adaptability to manage diverse wireless communications spectrum needs deteriorates
Solution Approach 1:
The patent implements a universal spectrum management system that can handle multiple frequency ranges and communication standards through a single platform. The system uses a database organized by frequency ranges and communication standards, allowing one device to perform functions that previously required multiple specialized devices. This resolves the contradiction by maintaining measurement precision through standardized analysis while achieving broad adaptability across diverse spectrum management needs.
2Adaptability or versatility
If comprehensive spectrum management systems are implemented to cover all frequency ranges, then adaptability is improved, but device complexity and bulkiness increase
Solution Approach 1:
The patent segments the spectrum management system into modular components organized by frequency ranges and communication standards. The database is divided into multiple frequency range tables (e.g., 0-1 GHz, 1-2 GHz, 2-3 GHz, 3-4 GHz, 4-5 GHz, 5-6 GHz, 6-7 GHz, 7-8 GHz, 8-9 GHz, 9-10 GHz), with each segment handling specific frequency bands. This segmentation allows the system to maintain comprehensive coverage while reducing complexity through organized, manageable modules.
Solution Approach 2:
A single universal database structure handles multiple frequency ranges and communication standards through standardized organization. The system uses unified tables and processing logic that can accommodate various frequency bands and communication protocols, eliminating the need for separate specialized systems for each band while maintaining manageable complexity through consistent architecture.
3Ease of manufacture
If existing spectral analyzer devices are used, then cost is reduced compared to comprehensive systems, but productivity and real-time analysis capability deteriorate
Solution Approach 1:
The system performs self-service through automated baseline data identification and changes-in-state detection. The database automatically stores and compares signal characteristics against baseline data, and the system autonomously identifies when changes occur without requiring manual analysis. This automation significantly improves real-time productivity while the system can be implemented at reasonable cost by leveraging standardized processing and database structures.
Solution Approach 2:
The system implements feedback mechanisms where detected signal changes are compared against stored baseline data, and the system automatically updates its understanding of the spectrum environment. This continuous feedback loop enables real-time detection and adaptation, improving productivity by allowing the system to respond dynamically to changing spectrum conditions without requiring expensive specialized hardware for each function.
Data Source
AI summary
Systems, methods, and apparatus are provided for automated identification of baseline data and changes in state in a wireless communications spectrum, by identifying sources of signal emission in the spectrum by automatically detecting signals, analyzing signals, comparing signal data to historical and reference data, creating corresponding signal profiles, and determining information about the baseline data and changes in state based upon the measured and analyzed data in near real time, which is stored on each apparatus or device and/or on a remote server computer that aggregates data from each apparatus or device.