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Resource-Constrained Specific Emitter Identification Based on Efficient Design and Network Compression.

Mengtao Wang¹, Shengliang Fang², Youchen Fan²

¹Graduate School, Space Engineering University, Beijing 101416, China.

Sensors (Basel, Switzerland)

|April 12, 2025

Summary

This summary is machine-generated.

This study introduces a resource-constrained specific emitter identification (RC-SEI) method using efficient design and model compression. The lightweight convolution network (LCNet) achieves high accuracy with significantly reduced model complexity for IoT applications.

Keywords:

deep learning (DL)lightweight convolution network (LCNet)radio frequency fingerprinting (RFF)sparse feature selection (SFS)specific emitter identification (SEI)

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Area of Science:

Signal Processing
Machine Learning
Computer Engineering

Background:

Deep learning (DL) enhances specific emitter identification (SEI) for complex signals.
High model complexity and feature dimensionality limit DL-based SEI on resource-constrained (RC) edge devices, particularly in Internet of Things (IoT).

Purpose of the Study:

To develop an efficient and compressed SEI method suitable for RC edge devices.
To address the limitations of model parameter redundancy and high feature dimensionality in DL-based SEI.

Main Methods:

Proposed a lightweight convolution network (LCNet) for efficient design, balancing performance and complexity.
Implemented sparse regularization in fully connected layers for over 99% feature dimensionality reduction.
Evaluated the method on public automatic-dependent surveillance-broadcast (ADS-B) and Wi-Fi datasets.

Main Results:

LCNet achieved high recognition accuracies: 99.40% on ADS-B and 99.90% on Wi-Fi.
The model demonstrated significantly reduced complexity with only 33,510 (ADS-B) and 33,544 (Wi-Fi) parameters.
The proposed method showed superior performance in both accuracy and model complexity compared to existing approaches.

Conclusions:

The developed RC-SEI method is feasible and effective for resource-constrained scenarios.
LCNet offers a promising solution for deploying advanced SEI in IoT applications.
Efficient design and model compression are crucial for practical edge AI implementations.