Unified Wireless Terminal Recognition Model
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Solution Overview
Problem
Existing wireless terminal recognition systems face inconsistencies in estimation results when attempting to identify multiple types of information (individual, model, and attribute) from received signals, particularly under low signal-to-noise ratios or varying environmental conditions, leading to decreased accuracy.
Innovation Solution
A transmission apparatus recognition apparatus that generates radio features from received signals and uses weighted-summing of similarity calculations across multiple template feature groups to estimate K kinds of information, including a predetermined kind and one or more other kinds, to reduce inconsistencies in estimation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If separate learning models are generated for individual estimation, model estimation and attribute estimation, then the device complexity is reduced and ease of operation is improved, but the reliability and measurement precision deteriorate under low signal-to-noise ratio conditions
Solution Approach 1:
The patent combines multiple separate learning models (individual estimation model, model estimation model, attribute estimation model) into a unified learning model that simultaneously estimates all three types of information. This unified model processes the radio feature vector through shared layers and produces multiple estimation outputs, thereby improving reliability and measurement precision under low signal-to-noise ratio conditions while maintaining manageable complexity through modular architecture design.
2Measurement precision
If multiple kinds of information are estimated simultaneously from the received signal, then the reliability and measurement precision are improved, but the device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent implements a universal learning model that performs multiple estimation functions (individual identification, model identification, and attribute estimation) simultaneously through a single unified architecture. The model accepts a radio feature vector as input and produces three distinct estimation outputs through shared processing layers, thereby achieving multi-functionality that improves measurement precision while managing system complexity through efficient resource sharing.
Solution Approach 2:
The unified learning model is segmented into distinct functional components: shared feature processing layers that handle common pattern recognition, and separate output heads that generate individual, model, and attribute estimations. This segmentation allows the system to manage complexity by organizing the multi-functionality into modular, independently trainable components while maintaining the benefits of joint learning.
3Ease of manufacture
If separate learning models are used for different information types, then the ease of manufacture and ease of operation are improved, but the loss of information increases due to inconsistent estimation results
Solution Approach 1:
The patent merges separate learning models into a unified model that simultaneously learns to estimate individual, model, and attribute information from the same radio feature input. This unified approach ensures information consistency by deriving all estimations from a common feature representation, eliminating the information loss and inconsistencies that arise from separate models making independent determinations about the same signal.
Data Source
AI summary
A transmission apparatus recognition apparatus includes a storage unit that stores K sets of template feature groups for estimating K (an integer of 2 or more) kinds of information indicative of a transmission apparatus, a degree-of-similarity calculation unit that generates an i (an integer of 1 to K)-th sample feature from a radio feature, and calculates an i-th degree-of-similarity group, based on the i-th sample feature and an i-th set of the template feature group, a summed degree-of-similarity calculation unit that calculates a summed degree of similarity by summing K degrees of similarity by using an i-th weighting factor with respect to 1 to K of i, and an estimation unit that estimates that K information pieces, which are correlated in advance with calculation sources of K degrees of similarity having the summed degree of similarity that is highest, are information indicative of the transmission apparatus.