Distributed ML for Primary Signal Detection in Multi-Area RF
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Solution Overview
Problem
Existing methods for detecting primary signals, such as radar signals, in shared spectrum environments are impractical due to the limitations of specialized standalone sensors in terms of cost, coverage, and operational constraints, making it challenging for cellular network operators to identify and respond to radar signals in a timely manner.
Innovation Solution
The implementation of distributed machine learning models on user equipment (UE) devices, which use deep unsupervised anomaly detection to identify primary signals by training and synchronizing machine learning models across UEs, allowing for continuous online learning and detection without the need for labeled data, and leveraging existing UE capabilities for efficient signal processing and communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If specialized standalone sensors are used to detect primary signals, then detection reliability is improved, but device complexity and cost increase
Solution Approach 1:
The patent segments the primary signal detection function across multiple user equipment devices rather than using a single centralized sensor system. Each UE device independently runs machine learning models to detect primary signals in its local vicinity, dividing the detection task into distributed segments that collectively provide comprehensive coverage
Solution Approach 2:
The patent makes user equipment devices perform multiple functions: their primary communication functions plus secondary primary signal detection functions. The UE devices use their existing RF front-ends and processors for both cellular communication and radar signal detection, eliminating the need for specialized standalone sensors
2Area of stationary object
If specialized standalone sensors are deployed to cover large geographical areas, then detection coverage is improved, but cost and operational constraints worsen
Solution Approach 1:
The patent implements self-service by enabling user equipment devices to autonomously perform primary signal detection without requiring external specialized sensors or infrastructure. The UE devices use their own built-in hardware and software resources to detect, analyze, and report primary signals, making the system self-sufficient and eliminating deployment complexities
Solution Approach 2:
The patent merges the primary signal detection function with existing user equipment devices that are already deployed for communication purposes. By combining secondary signal operation with primary signal detection capabilities in the same devices, the system achieves large-area coverage without additional sensor deployment
3Ease of manufacture
If distributed machine learning models are used on UE devices, then detection cost is reduced, but measurement precision may worsen
Solution Approach 1:
The patent uses copying by deploying identical machine learning models across multiple user equipment devices. Each UE device receives and executes the same anomaly detection model, allowing the system to achieve comprehensive coverage while maintaining consistent detection capabilities across all devices without requiring expensive specialized hardware
Solution Approach 2:
The patent implements feedback mechanisms where anomaly detection results from distributed UE devices are collected and analyzed by a secondary signal operator. The system uses feedback from multiple devices to improve detection accuracy, validate anomalies, and reduce false positives, thereby maintaining measurement precision despite using cost-effective distributed machine learning
Data Source
AI summary
Methods and systems for primary signal detection using distributed machine learning in a multi-area environment are disclosed. In an example method, it is determined that a first user equipment (UE) device moved to a first predefined area from a second predefined area. A controller sends, to the first UE device, a first machine learning model configured to detect an anomaly in an RF environment associated with the first area. The first machine learning model may have been determined by a second UE device associated with the first area. The controller receives, from the first UE device, anomaly data indicative of an anomaly detected by the first UE device via the first machine learning model. The controller may optionally determine that a primary signal is present in an RF environment associated with the first area based on the anomaly data from the first UE device.