Machine-Learning SAS Commands for 3.5 GHz CBRS Interference
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Solution Overview
Problem
Existing Spectrum Administration Service (SAS) systems struggle to dynamically manage Citizens Broadband Radio Service (CBRS) spectrum in the 3.5 GHz band, leading to interference and suboptimal communication quality due to inefficient channel allocation and interference management across different tiers.
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 channel frequencies and prevent interference, thereby improving communication quality and processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional SAS systems manually manage spectrum allocation across tiers, then interference management can be performed, but the system complexity and processing time increase significantly
Solution Approach 1:
The system enables self-service by implementing automated machine learning models that independently analyze channel parameters, predict interference patterns, and generate optimization commands without requiring manual SAS intervention. The ML algorithm autonomously processes spectrum data and adjusts allocations across tiers, reducing operational complexity while maintaining reliable interference management.
Solution Approach 2:
The patent replaces traditional mechanical/manual spectrum management processes with machine learning-based automated systems. The ML algorithm substitutes manual configuration and analysis with intelligent computational models that process channel parameters and generate optimization commands, significantly reducing system complexity and processing time.
2Ease of operation
If existing SAS systems use static channel allocation, then configuration is simpler, but communication quality deteriorates due to interference and changing conditions
Solution Approach 1:
The system implements dynamic channel allocation where the ML algorithm continuously monitors channel parameters and adjusts spectrum assignments in real-time based on changing conditions. This dynamic approach maintains communication quality by adapting to interference patterns and traffic demands while keeping configuration management automated and simple through the intelligent system.
Solution Approach 2:
The patent incorporates feedback mechanisms where the ML algorithm continuously analyzes channel performance metrics and uses this information to generate optimization commands. The system monitors communication quality indicators and adjusts spectrum allocations accordingly, creating a closed-loop control system that maintains high communication quality with automated configuration management.
3Speed
If SAS systems process channel parameters in real-time without optimization, then responsiveness is maintained, but processing speed and efficiency decrease
Solution Approach 1:
The system applies preliminary action by pre-training machine learning models with historical spectrum data and channel parameters before deployment. The ML algorithm learns optimal processing patterns in advance, enabling it to quickly analyze real-time channel data and generate optimization commands without extensive real-time computation, thus improving both processing speed and management efficiency.
Solution Approach 2:
The patent utilizes parameter changes by transforming raw channel parameters into optimized spectrum allocation decisions through the ML algorithm. The system processes channel parameters such as frequency, bandwidth, and power levels, transforming them into optimized configuration commands that improve spectrum management efficiency while maintaining rapid processing speeds through intelligent computation.
4Stability of the object's composition
If traditional systems react to interference after it occurs, then stability is maintained, but downtime and service disruption increase
Solution Approach 1:
The system implements preliminary anti-action by using the ML algorithm to predict potential interference conditions before they occur. The algorithm analyzes channel parameters and identifies patterns that precede interference events, allowing the SAS to proactively generate optimization commands that prevent interference before it degrades service, thus maintaining network stability and eliminating downtime.
Solution Approach 2:
The patent applies preliminary action by continuously monitoring channel parameters and using the ML algorithm to anticipate interference conditions. The system prepares and executes optimization commands in advance based on predictive analysis, preventing service disruption before it occurs and maintaining continuous network stability without downtime.
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.