Tiered Spectrum Assignment for Unused Network Resource Reallocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless communication systems face inefficiencies in network resource utilization, leading to dropped communication operations and service interruptions due to insufficient available resources, which can be exacerbated by increased network traffic demands.
Innovation Solution
Implementing machine learning algorithms and artificial intelligence commands to optimize and dynamically assign network resources, including power, memory, and processing resources, across communication sites, allowing for proactive reassignment and tiered allocation based on consumption requirements and demand, with a focus on improving spectrum efficiency and reducing downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network resources are allocated to handle increased traffic demands, then communication operations can be maintained, but total available network resources are drained and spectrum utilization efficiency decreases
Solution Approach 1:
The patent implements dynamic spectrum assignment where network resources are not statically allocated but continuously adjusted based on real-time traffic conditions. The system monitors spectrum usage patterns and dynamically reassigns frequency resources to different communication channels as needed, allowing the network to adapt to varying traffic demands while maintaining efficient spectrum utilization. This dynamic approach resolves the contradiction by enabling reliable communication during high traffic without permanently draining spectrum resources.
Solution Approach 2:
The system changes spectral parameters (frequency assignments, bandwidth allocations) based on observed usage patterns and traffic conditions. By modifying these parameters dynamically rather than maintaining fixed allocations, the network can handle increased traffic demands while preserving spectrum efficiency. The patent specifically mentions optimizing spectral efficiency through parameter adjustments in response to traffic patterns.
2Reliability
If network resources are increased to prevent dropped communication operations, then service reliability improves, but network cost and resource consumption increase
Solution Approach 1:
The patent implements self-service mechanisms where the network system automatically monitors its own resource usage, identifies underutilized spectrum resources, and reallocates them to areas of high demand without external intervention. The system uses machine learning algorithms to autonomously detect patterns and make optimization decisions, enabling the network to maintain service reliability while preventing resource waste through automated self-optimization.
Solution Approach 2:
The system employs feedback loops where spectrum usage data is continuously collected, analyzed, and used to adjust resource allocation decisions. The machine learning models process real-time feedback on communication operation success rates and spectrum utilization, then automatically adjust assignments to prevent dropped operations while optimizing resource efficiency. This closed-loop feedback mechanism ensures reliability improvements without proportional increases in resource consumption.
3Device complexity
If static spectrum assignment is used to simplify network management, then system complexity is reduced, but spectrum utilization efficiency and adaptability decrease
Solution Approach 1:
The patent replaces manual or mechanical spectrum management approaches with automated machine learning-based systems. Instead of relying on complex human-configured static assignments, the system uses AI algorithms to automatically optimize spectrum allocation. This substitution of mechanical management with intelligent automation reduces operational complexity while dramatically improving spectrum utilization efficiency through adaptive optimization.
Solution Approach 2:
The machine learning platform serves multiple functions simultaneously: it monitors spectrum usage, predicts traffic patterns, optimizes resource allocation, and prevents dropped operations. This multi-functional universal system handles various network management tasks through a single integrated platform, reducing overall system complexity while improving productivity through coordinated optimization across multiple functions.
Data Source
AI summary
An apparatus comprises a memory and a processor communicatively coupled to one another. In response to obtaining spectrum resource assignment information, the processor is configured to execute a machine learning algorithm to analyze the spectrum resource assignment information based at least in part upon multiple communication conditions, generate multiple analysis results in response to analyzing the spectrum resource assignment information, determine one or more unused resources in a communication spectrum over a period of time, generate multiple spectrum assignment recommendations based at least in part upon the analysis results, and assign second resources in the communication spectrum for the one or more communication sites over a second period of time in accordance with the spectrum assignment recommendations and a first priority level.