AI-Based Spectrum Reassignment for Network Upgrade Windows
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently managing network resources, leading to increased traffic and dropped communication operations due to resource shortages, which can cause service interruptions and inefficiencies.
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 based on consumption demands and utilization patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If network resources are allocated to handle increased traffic demand, then communication operations can be completed, but the total number of available network resources is drained
Solution Approach 1:
The patent implements dynamic resource allocation where the network manager continuously monitors traffic patterns and dynamically adjusts resource assignments across communication sites. Resources are reallocated based on real-time demand, allowing the system to adapt to changing traffic conditions without being constrained by fixed resource allocations, thus resolving the contradiction between handling increased traffic and preserving available resources.
Solution Approach 2:
The system changes resource allocation parameters based on traffic demand analysis. By modifying allocation decisions based on monitored traffic patterns and predicted demands, the network can optimize the distribution of resources across different communication sites, enabling higher productivity while conserving the total quantity of network resources through intelligent parameter adjustment.
2Reliability
If network resources are increased to prevent dropped operations, then service reliability improves, but the cost and complexity of network management increases
Solution Approach 1:
The network manager autonomously performs resource allocation decisions by monitoring traffic patterns and automatically adjusting assignments without requiring manual intervention. The system self-regulates resource distribution based on real-time conditions, maintaining service reliability while reducing the operational complexity of network management through automated decision-making processes.
Solution Approach 2:
The system continuously monitors traffic patterns and uses this feedback to adjust resource allocations. By implementing closed-loop control where performance data feeds back into allocation decisions, the network maintains high reliability through adaptive resource management while avoiding the complexity of manual resource adjustment processes.
3Productivity
If manual scheduling of network upgrades is used, then resource allocation can be optimized, but downtime and service interruptions increase
Solution Approach 1:
The system performs preliminary analysis of traffic patterns and predicts future resource needs before implementing upgrades or reallocations. By planning resource adjustments in advance based on monitored trends, the network can schedule changes during optimal times to minimize downtime while maintaining allocation efficiency, resolving the contradiction between optimized resource management and service continuity.
Data Source
AI summary
An apparatus comprises a memory and a processor communicatively coupled to one another. The processor may be configured to obtain telemetry data for at least one communication site of the one or more communication sites. Further, in response to obtaining the telemetry data, the processor may be configured to execute the machine learning algorithm to analyze the spectrum resource assignment information and the telemetry data based at least in part upon multiple communication conditions, generate multiple analysis results in response to analyzing the spectrum resource assignment information and the telemetry data, determine a release window based at least in part upon the analysis results, 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.