Distributed ML Rogue Base Station Detection From Handover Failures
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Solution Overview
Problem
Existing methods for detecting rogue base stations in telecommunications networks face challenges such as privacy issues and delays in detection due to frequent UE reporting and centralized AI/ML models, which can lead to misidentification of handover failures and blacklisting of genuine base stations.
Innovation Solution
A distributed machine learning solution is implemented, splitting the machine learning model into a first ML partition running at the UE level and a second ML partition at the network level, tailored to each UE's specifications, to detect rogue base stations by analyzing handover failures and UE measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized AI/ML models are used for rogue base station detection, then detection capability is improved, but detection delays occur and privacy issues arise
Solution Approach 1:
The centralized AI/ML model is segmented into multiple distributed model partitions deployed across different network entities (gNBs, UEs, edge servers). This segmentation enables parallel processing of detection tasks, reducing detection delay while maintaining detection capability through coordinated analysis across the distributed system.
Solution Approach 2:
The detection system transitions from a single centralized dimension to multiple distributed dimensions by deploying model partitions across different network locations (core network, edge network, user equipment). This multi-dimensional distributed architecture reduces detection delay through spatial parallelism while preserving privacy through localized data processing.
2Measurement precision
If frequent UE reporting is implemented, then detection accuracy is improved, but privacy issues worsen
Solution Approach 1:
Different model partitions are deployed to different locations (gNB, UE, edge server) based on local capabilities and requirements. Each location processes data locally with appropriate privacy protection, while contributing to overall detection accuracy through coordinated analysis. This local quality approach allows frequent reporting where needed while preserving privacy where possible.
Solution Approach 2:
The distributed model partition architecture acts as an intermediary between UE measurements and centralized detection. Model partitions at intermediate nodes (gNB, edge server) aggregate and process measurements before final analysis, reducing the need for frequent direct UE reporting to the core network and thereby protecting user privacy while maintaining detection accuracy.
3Productivity
If distributed ML model partitions are deployed, then detection speed is improved, but device complexity increases
Solution Approach 1:
The distributed model partition deployment is designed to be dynamic and adaptive. Model partitions can be selectively deployed, updated, or removed based on network conditions, device capabilities, and security requirements. This dynamic approach enables fast detection where resources permit while reducing complexity in constrained environments, resolving the contradiction between detection speed and deployment complexity.
Data Source
AI summary
An apparatus configured to: obtain an indication of partitions of a machine learning model corresponding to respective ones of the one or more groups; transmit, to the respective ones of the plurality of user equipments, a corresponding partition, of the partitions of the machine learning model; transmit, to the plurality of user equipments, an indication to record measurements for the first cell; receive, from at least one of the plurality of user equipments, at least one message regarding a handover failure, wherein the at least one message comprises a message generated using a first partition of the partitions of the machine learning model; and determine, with a second partition of the partitions of the machine learning model, whether the first cell is a rogue base station based, at least partially, on a plurality of detection reports.