PLS-YOLO: A Lightweight Model for Signal Modulation Recognition

ZHOU Xiaobo; ZHANG Fan; SHE Chao; ZHOU Guofei; MENG Jianping

doi:10.11999/JEIT251377

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ZHOU Xiaobo, ZHANG Fan, SHE Chao, ZHOU Guofei, MENG Jianping. PLS-YOLO: A Lightweight Model for Signal Modulation Recognition[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251377

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ZHOU Xiaobo, ZHANG Fan, SHE Chao, ZHOU Guofei, MENG Jianping. PLS-YOLO: A Lightweight Model for Signal Modulation Recognition[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251377

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PLS-YOLO: A Lightweight Model for Signal Modulation Recognition

doi: 10.11999/JEIT251377 cstr: 32379.14.JEIT251377

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)

Received Date: 2025-12-30
Accepted Date: 2026-04-17
Rev Recd Date: 2026-04-16

Available Online: 2026-05-06

Abstract

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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References(24)

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