SAS Configuration Command Optimization for CBRS Interference Control
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Solution Overview
Problem
Existing wireless communication systems face challenges in optimizing Spectrum Administration Service (SAS) operations to manage Citizens Broadband Radio Service (CBRS) spectrum efficiently, particularly in preventing interference across different tiers and improving processing speeds.
Innovation Solution
A system and method that utilizes a machine learning algorithm to analyze channel parameters stored in a data lake, generating optimized SAS configuration commands to dynamically adjust communication channels and prevent interference, thereby improving processing speeds and reducing network stress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional SAS operations are used to manage CBRS spectrum, then spectrum coordination across tiers is maintained, but processing speeds are slow and network stress increases during high traffic conditions
Solution Approach 1:
The machine learning algorithm analyzes historical channel parameters and traffic patterns to proactively generate optimized SAS configuration commands before interference or network stress occurs. This preliminary action enables the system to preemptively adjust spectrum allocation and routing, preventing problems rather than reacting to them, thus improving processing speed while maintaining network reliability.
Solution Approach 2:
The system continuously monitors channel parameters from the data lake and uses this feedback to dynamically adjust SAS configuration commands. The machine learning algorithm processes real-time and historical data to identify patterns and optimize spectrum management decisions, creating a closed-loop system that improves processing speed through adaptive decision-making while ensuring network reliability through continuous monitoring and adjustment.
2Reliability
If dynamic spectrum management is implemented to prevent interference across tiers, then spectrum coordination is improved, but system complexity increases
Solution Approach 1:
The machine learning algorithm autonomously analyzes channel parameters, identifies interference patterns, and generates optimized SAS configuration commands without requiring complex manual intervention or additional coordination infrastructure. The system serves itself by automatically learning from historical data and making intelligent decisions, thereby improving interference prevention while minimizing the increase in system complexity.
Solution Approach 2:
The system optimizes spectrum management by dynamically changing parameters such as channel allocation, frequency assignment, and routing configurations based on learned patterns from historical data. Rather than implementing complex structural changes, the system achieves improved interference prevention through intelligent parameter adjustment, thereby reducing the burden of system complexity.
3Productivity
If machine learning algorithms are used to optimize SAS configuration commands, then processing speeds improve and network stress is reduced, but computational requirements and energy consumption increase
Solution Approach 1:
The machine learning algorithm processes only the most critical and relevant channel parameters from the data lake to generate optimized SAS configuration commands, rather than analyzing every available data point. This partial action approach maintains high processing speeds and reduces network stress while minimizing computational energy consumption by focusing computational resources on the most impactful parameters.
4Reliability
If historical channel parameters are stored and analyzed to predict future interference, then network operation continuity is improved, but data storage requirements increase
Solution Approach 1:
The machine learning algorithm extracts and analyzes only the most relevant and discriminative channel parameters from the historical data stored in the data lake, rather than processing the entire dataset. This extraction approach enables the system to improve network operation continuity through accurate interference prediction while minimizing data storage requirements by focusing only on the essential parameters needed for reliable predictions.
Data Source
AI summary
An apparatus comprises a memory and a processor communicatively coupled to one another. The memory may be configured to store a data lake and multiple existing spectrum administration service (SAS) configuration commands. The processor may be configured to perform first SAS operations in accordance with the existing SAS configuration commands, collect multiple channel parameters from one or more communication channels configured to provide connectivity between user equipment and a core network, store the channel parameters in the data lake, monitor the channel parameters in the data lake, and generate optimized SAS configuration commands based at least in part upon the channel parameters. Further, the processor is configured to compare the optimized SAS configuration commands to the existing SAS configuration commands and perform second SAS operations in accordance with the optimized SAS configuration commands.