Work

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

Year: 2025

Type: article

Abstract: Specific emitter identification (SEI) methods based on deep learning (DL) have effectively addressed complex, multi-dimensional signal recognition tasks by leveraging deep neural networks. However, this advancement introduces challenges such as model parameter redundancy and high feature dimensionality, which pose limitations for resource-constrained (RC) edge devices, especially in Internet of Things (IoT) applications. To tackle these problems, we propose an RC-SEI method based on efficient design and model compression. Specifically, for efficient design, we have developed a lightweight convolution network (LCNet) that aims to balance performance and complexity. Regarding model compression, we introduce sparse regularization techniques in the fully connected (FC) layer, achieving over 99% feature dimensionality reduction. Furthermore, we have comprehensively evaluated the proposed method on public automatic-dependent surveillance-broadcast (ADS-B) and Wi-Fi datasets. Simulation results demonstrate that our proposed method exhibits superior performance in terms of both recognition accuracy and model complexity. Specifically, LCNet achieved accuracies of 99.40% and 99.90% on the ADS-B and Wi-Fi datasets, respectively, with only 33,510 and 33,544 parameters. These results highlight the feasibility and potential of our proposed RC-SEI method for RC scenarios.

Source: Sensors

DOI: 10.3390/s25072293

Authors: Mengtao Wang, Shengliang Fang, Youchen Fan, Shunhu Hou

Institution: Space Engineering University

Language: English

Topic: Wireless Signal Modulation Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137