Electromagnetic Environment Learning for Dynamic Spectrum Allocation
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Solution Overview
Problem
Effective spectrum management is hindered by the diverse nature of wireless signal propagations across different frequencies and technological standards, and the growing demand for spectrum exceeds the finite available resources, necessitating efficient utilization.
Innovation Solution
A system for dynamic spectrum utilization management in private wireless networks, incorporating monitoring sensors, data analysis engines, and learning engines to detect signals of interest, learn the electromagnetic environment, and optimize network resources using actionable data to reconfigure parameters of the core network and Multi-Access Edge Computing layer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If spectrum management systems attempt to manage all wireless signals across different frequencies and standards, then spectrum utilization improves, but system complexity increases significantly
Solution Approach 1:
The system segments the complex spectrum management task by separating signal detection, classification, and resource allocation into distinct functional modules. Monitoring sensors detect signals independently, data analysis engines classify them by type and protocol, and then resource allocation decisions are made based on classified information. This segmentation allows the system to manage diverse wireless signals without requiring a single complex processing unit to handle all aspects simultaneously.
Solution Approach 2:
The patent introduces intermediary components including monitoring sensors that act as mediators between the electromagnetic environment and the management system, and data analysis engines that serve as intermediaries between raw signal data and resource allocation decisions. These intermediaries simplify the overall system architecture by processing and structuring information in stages, making the management of diverse wireless standards more tractable.
2Productivity
If the system monitors and analyzes all electromagnetic signals in the environment, then resource optimization improves, but data processing requirements and time consumption increase
Solution Approach 1:
The system performs preliminary classification of electromagnetic signals by type and protocol before detailed resource allocation analysis. Monitoring sensors continuously detect signals and categorize them into known types (WiFi, cellular, Bluetooth, etc.), creating a structured database of signal characteristics. This preliminary action allows the resource allocation engine to quickly reference pre-classified information rather than analyzing raw signals from scratch each time, significantly reducing processing time while maintaining optimization accuracy.
Solution Approach 2:
The system implements feedback mechanisms where classification results and resource allocation outcomes are fed back into the monitoring and analysis processes. The data analysis engines learn from patterns in classified signals and adjust their classification criteria over time, improving processing efficiency. Additionally, resource allocation decisions are refined based on feedback about actual network performance and signal conditions, enabling faster and more accurate optimization without increasing initial processing burden.
Data Source
AI summary
Systems, methods, and apparatuses for providing optimization of network resources. The system is operable to monitor the electromagnetic environment, analyze the electromagnetic environment, and extract environmental awareness of the electromagnetic environment. The system extracts the environmental awareness of the electromagnetic environment by including customer goals. The system is operable to use the environmental awareness with the customer goals and/or user defined policies and rules to extract actionable information to help the customer optimize the network resources.