PLS-YOLO：一种轻量化的信号调制识别模型

周晓波; 张凡; 佘超; 周郭飞; 孟建平

doi:10.11999/JEIT251377

PLS-YOLO：一种轻量化的信号调制识别模型

doi: 10.11999/JEIT251377 cstr: 32379.14.JEIT251377

1.
北京交通大学电子信息工程学院北京 100044
2.
公安部第一研究所北京 100048

基金项目: 国家自然科学基金(52472174)，国家重点研发计划(2023YFB3208101)

详细信息

作者简介:
周晓波：男，副教授，研究方向为信号处理与专用集成电路设计

张凡：女，硕士生，研究方向为信号处理与数字集成电路设计

佘超：男，工程师，研究方向为信号处理与电路设计

周郭飞：男，研究员，研究方向为微弱信号处理技术与集成电路设计

孟建平：男，教授，研究方向为忆阻器与神经形态计算

通讯作者:
张凡　24125011@bjtu.edu.cn

中图分类号: TN929.5
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- 被引次数: 0
出版历程
- 收稿日期: 2025-12-30
- 修回日期: 2026-04-16
- 录用日期: 2026-04-17
- 网络出版日期: 2026-05-06

PLS-YOLO: A Lightweight Model for Signal Modulation Recognition

1.
School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
2.
The First Research Institute of the Ministry of Public Security, Beijing 100048, China

Funds: The National Natural Science Foundation of China (52472174), The National Key Research and Development Program of China (2023YFB3208101)

摘要

摘要: 自动调制识别是无线通信频谱监测与安全保障的关键技术。针对当前基于深度学习的自动调制识别模型难以兼顾高识别准确率与低模型复杂度的问题，该文提出一种基于视觉目标检测的轻量化识别方法。首先利用短时傅里叶变换将IQ信号转化为时频图，并采用拼图形式进行预处理，将调制识别任务转化为视觉目标检测问题。随后以YOLOv10n为基座，构建了精度与轻量化结构YOLO(PLS-YOLO)模型。该模型通过重构网络核心模块、优化主干网络通道降维策略、设计新型下采样结构以及改进注意力模块的前馈网络，有效实现了识别性能与轻量化结构的平衡。在RadioML2016.10a和RadioML2016.10b数据集上的实验结果表明，PLS-YOLO的平均精度分别达到68.4%和73.30%。与YOLOv10n相比，其参数量减少了47.33%，浮点运算次数降低了34.15%，且帧率提升了5 帧/s。研究结果证实，PLS-YOLO模型在显著降低计算成本的同时，依然保持了优异的识别精度。
- 深度学习 /
- 信号调制方式识别 /
- 轻量化 /
- YOLOv10
Abstract: Objective As wireless communication evolves toward high efficiency, low latency, and ubiquitous connectivity, higher requirements are placed on Automatic Modulation Recognition (AMR) to ensure link reliability in complex electromagnetic environments. Deep learning has improved recognition performance compared with traditional methods, which often rely on subjective feature design and have limited robustness. However, existing YOLO-based AMR models are not fully optimized for specific signal characteristics or practical deployment. These models often have excessive parameters and high computational complexity, which makes them unsuitable for resource-constrained hardware, such as edge nodes and Field-Programmable Gate Arrays (FPGAs), and limits their ability to meet real-time communication requirements. To address these bottlenecks, this paper proposes Precision and Lightweight Structure-YOLO (PLS-YOLO), a lightweight AMR model based on YOLOv10n. By optimizing network channels, replacing core modules, and improving the downsampling mechanism, the proposed model enables efficient integration of modulation signal classification and localization. It also reduces the parameter count and computational complexity, thereby supporting AMR deployment in resource-constrained scenarios. Methods The method includes two main stages: dataset preprocessing and PLS-YOLO model construction. In the preprocessing stage, the public RadioML2016.10a and RadioML2016.10b benchmark datasets for signal modulation recognition are used. For In-phase and Quadrature (IQ) signals in these datasets, the Short-Time Fourier Transform (STFT) is used to map one-dimensional temporal signals into two-dimensional time-frequency spectrograms containing phase and amplitude information. This process provides richer feature representations for the model. A random sampling strategy without replacement is then used to stitch individual time-frequency samples into 3×3 composite images (Fig. 4). Target labels matching the input format of YOLO-series models are generated at the same time. The dataset is divided into training, validation, and test sets at a ratio of 7:1.5:1.5 by stratified sampling to ensure consistent signal-type distributions across all subsets. The model is built on YOLOv10n, with targeted improvements designed to balance the parameter count and recognition performance. The C2f module in the original backbone network is replaced with the CSPPC module, which is based on the CSP architecture and consists of feature splitting, Partial Convolution (PConv) processing, and feature fusion. This design reduces parameters while improving recognition performance. The feature dimensionality reduction process in the backbone network is also reconstructed to reduce the increase in computational complexity caused by parameter redundancy. The traditional downsampling module is replaced with CGBlock, which improves the capture of complex modulation signal features by fusing context-aware information. Finally, standard convolutions in the PSA and v10Detect modules are replaced with PConv to further reduce computational complexity and jointly optimize lightweight design and recognition performance. Results and Discussions Experimental results on RadioML2016.10a show that PLS-YOLO achieves a mean Average Precision (mAP) of 68.4% within the Signal-to-Noise Ratio (SNR) range of –20 to 18 dB. The mAP increases to 94.3% when SNR ≥ 0 dB. Compared with the baseline YOLOv10n model, PLS-YOLO improves mAP by 0.6%, reduces the parameter count by 47.33%, and decreases computational complexity by 34.15%. Its inference speed also increases by 5 frames per second (fps) (Table 2). These results show that the model effectively balances recognition performance and lightweight deployment by reducing computational cost while improving precision. To verify robustness, additional experiments are conducted on RadioML2016.10b. As shown in Table 4, PLS-YOLO achieves an mAP of 73.30% over the –20 to 18 dB range and 95.4% at SNR ≥ 0 dB. It outperforms mainstream models such as MCNet and LSTM2, confirming its strong recognition performance. Furthermore, Fig. 5 shows that converting IQ data into spectrograms is more suitable for PLS-YOLO recognition of digital modulation signals. By contrast, the recognition performance for analog modulation signals remains limited. Future work should therefore improve feature modeling and recognition capability for analog signals. Conclusions This study proposes PLS-YOLO, a lightweight AMR model based on YOLOv10n. To jointly improve modulation recognition performance and model compactness, the network structure is optimized through channel dimensionality reduction, core module replacement, downsampling mechanism improvement, and PConv substitution. These strategies reduce key limitations of existing YOLO-based AMR models, including parameter redundancy, high computational complexity, and limited adaptability to resource-constrained scenarios such as edge nodes and FPGAs. Experiments on the RadioML2016.10a and RadioML2016.10b benchmark datasets show that PLS-YOLO achieves strong overall performance. While integrated signal classification and localization are maintained, both parameter count and computational complexity are substantially reduced compared with the baseline YOLOv10n model, with a clear improvement in recognition performance. The results verify the effectiveness and feasibility of the proposed optimization strategies and provide a practical technical path for AMR implementation. The remaining limitations in analog modulation signal recognition also indicate a clear direction for future research.
- Deep learning /
- Automatic modulation recognition /
- Lightweight /
- YOLOv10

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图 1 PLS-YOLO网络结构图

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图 2 部分卷积架构

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图 3 CSPPC架构

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图 4 拼图后数据

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图 5 各个调制方式下不同信噪比识别率

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图 6 混淆矩阵

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表 1 RadioML2016.10a数据集

类型	数据
样本数量	总样本数：220 000，单调制数：20 000
调制方式	PSK, BPSK, CPFSK, GFSK, 4-PAM, 16-QAM, 64-QAM, QPSK, AM-DSB, AM-SSB, WBFM
信噪比范围	–20～18 dB，间隔为2 dB
样本大小	(2, 128)

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表 2 主流YOLO模型对比

模型	P	R	mAP	fps	FLOPs(G)	参数量	权重文件(MB)
YOLOv8n	0.721	0.625	0.671	121	8.1	3 007 793	5.4
YOLOv10n	0.746	0.612	0.678	119	8.2	2 635 602	5.8
YOLOv10s	0.750	0.620	0.681	109	24.5	8 043 474	15.7
PLS-YOLO	0.735	0.632	0.684	124	5.4	1 388 277	3.4

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表 3 消融实验对比

CSPPC	PCF	CGBlock	GPSA+ v10PDetect	mAP	FLOPs(G)	参数量
×	×	×	×	0.678	8.2	2635602
√	×	×	×	0.681	6.9	2245394
×	√	×	×	0.681	6.9	1920754
×	×	√	×	0.687	8.1	2699033
×	×	×	√	0.673	8.2	2625682
√	√	×	×	0.681	5.6	1404338
√	√	√	×	0.685	5.4	1457333
√	√	√	√	0.684	5.4	1388277

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表 4 PLS-YOLO与当前主流AMR算法的性能对比

数据集	模型	识别精度(%)		数据集	模型	识别精度(%)
数据集	模型	0～18 dB	–20～18 dB	数据集	模型	0～18 dB	–20～18 dB
10a	MCNET^[17]	82.33	56.63	RadioML 10b	MCNET^[17]	85.69	60.95
	LSTM2^[18]	91.41	60.56		LSTM2^[18]	88.65	63.28
	PET-CGDNN^[19]	89.45	60.38		PET-CGDNN^[19]	92.64	63.91
	MCLDNN^[20]	91.68	61.93		MCLDNN^[20]	93.02	64.46
	AMC-NET^[21]	91.32	62.40		AMC-NET^[21]	93.34	65.14
	FEA-T^[22]	88.54	60.55		FEA-T^[22]	93.12	64.44
	多模态联合两阶段网络^[23]	93.36	64.52		多模态联合两阶段网络^[23]	93.13	65.59
	文献[24]	92.06	62.44		文献[24]	93.24	64.61
	PLS-YOLO	94.30	68.40		PLS-YOLO	95.40	73.30

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