Machine Learning-Based Interference Detection for Hierarchical Licensing Deployment
JP2025520276A5Pending Publication Date: 2026-05-22MICROSOFT TECHNOLOGY LICENSING LLC
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Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- MICROSOFT TECHNOLOGY LICENSING LLC
- Filing Date
- 2023-05-10
- Publication Date
- 2026-05-22
AI Technical Summary
Technical Problem
In a hierarchical licensing system like CBRS, General Authorized Access (GAA) users face interference from other GAA users without protection, and conventional methods like CCA and LBT are inadequate, necessitating advanced interference detection and mitigation techniques.
Method used
A system utilizing a local spectrum access database and machine learning-based source separation algorithms to identify and mitigate interference signals from other GAA users by analyzing IQ samples, shifting carrier frequencies, and adding interference signals to the database to avoid interference.
Benefits of technology
Effectively detects and mitigates interference from other GAA users, enhancing spectrum utilization and reducing interference-related issues in GAA systems.
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Abstract
Examples for machine learning-based interference detection for hierarchical licensing deployment are described. Network entities in a General Authorized Access (GAA) deployment check a local spectrum access database of GAA users to determine that a portion of the shared spectrum is freed from known local users in a geographic area. The network entity receives samples of wireless signals containing at least a desired signal on a portion of the shared spectrum. The network entity determines whether the wireless signal contains a plurality of unrelated signals. The network entity identifies an interference signal in response to determining that the wireless signal contains a plurality of unrelated signals.
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