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

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

Available Online: 2026-05-06

Abstract

Abstract

Objective As wireless communication evolves toward high communication efficiency, low latency, and ubiquitous connectivity, stringent demands are deployed on Automatic Modulation Recognition (AMR) technology to ensure link reliability within complex electromagnetic environments. While deep learning has significantly enhanced recognition accuracy compared to traditional methods, which are often characterized by high subjectivity and poor robustness, existing YOLO-based AMR models remain unoptimized for specific signal characteristics and deployment scenarios. These models typically suffer from excessive parameters and high computational complexity, rendering them unsuitable for resource-constrained hardware such as edge nodes and FPGAs, and unable to meet real-time communication demands. A lightweight AMR model based on YOLOv10n, denoted as PLS-YOLO, is proposed to resolve the critical bottlenecks that restrict practical deployment of AMR techniques. By employing target strategies such as optimizing network channel, replacing core modules, and enhancing down-sampling mechanism, the integration of modulation signal classification and localization are realized swiftly. Furthermore, significant reductions in model parameters and computational complexity are achieved, thereby facilitating the adaptation of AMR models to resource-limited scenarios and providing technical support. Methods The experimental methodology centers on two core stages: dataset preprocessing and the construction of the PLS-YOLO model. In the preprocessing phase, the public benchmark datasets RadioML2016.10a and RadioML2016.10b from the field of signal modulation recognition are utilized as the foundation. For the In-phase and Quadrature (IQ) signals within these datasets, the Short-Time Fourier Transform (STFT) is employed to map one-dimensional temporal signals into two-dimensional time-frequency spectrograms containing critical information such as phase and amplitude, thereby providing richer feature representations for the model. Subsequently, a random sampling strategy without replacement is adopted to stitch single time-frequency samples into 3×3 aggregated images (Fig. 4), while target labels matching the input format of YOLO series models are synchronously generated. The dataset is ultimately partitioned into training, validation, and test sets at a ratio of 7:1.5:1.5 via stratified sampling to ensure the consistency of signal type distribution across all subsets. The model construction is based on the YOLOv10n architecture, with specific improvements implemented to achieve balance between parameter quantity and recognition performance in modulation recognition tasks. The C2f module in the original backbone network is replaced by the CSPPC module, based on the CSP architecture and comprising feature splitting, partial convolution processing, and feature fusion, to achieve the dual objectives of parameter reduction and recognition rate enhancement. Furthermore, the feature dimensionality reduction process of the backbone network is reconstructed to effectively mitigate the surge in computational load caused by parameter redundancy. The traditional down-sampling module is replaced by the innovative CGBlock, enhancing the capability to capture features of complex modulation signals by fusing context-aware information, thereby elevating recognition performance. Finally, standard convolutions in the PSA module and the v10Detect module are replaced with Partial Convolutions to further reduce computational complexity, realizing a synergistic optimization of lightweight design and recognition performance. Results and Discussions Experimental results on the RadioML2016.10a dataset indicate that the PLS-YOLO model achieves a mean Average Precision (mAP) of 68.4% within the signal-to-noise ratio (SNR) range of -20 to 18 dB, which further increases to 94.3% when the SNR is no less than 0 dB. Compared with the basic YOLOv10n model, PLS-YOLO attains a slight mAP improvement of 0.6% while reducing the parameter count by 47.33% and computational complexity by 34.15%, alongside an increase in inference speed by 5 FPS (Table 2). These findings verify that the model effectively balances performance with lightweight requirements by significantly decreasing computational costs while enhancing precision. To validate robustness, supplementary experiments are conducted on the RadioML2016.10b dataset. As shown in Table 4, the model achieves an mAP of 72.6% across the -20 to 18 dB range and 95.4% for SNR ≥ 0 dB, outperforming mainstream models such as MCNET and LSTM2, thereby demonstrating the superior performance of PLS-YOLO. Furthermore, as illustrated in Fig.5, it is observed that converting IQ data into spectrograms for PLS-YOLO recognition is more adaptive to digital modulation signals, whereas performance on analog modulation signals remains suboptimal; consequently, future research should focus on enhancing the recognition capabilities for analog signals. Conclusions This study proposes PLS-YOLO, a lightweight Automatic Modulation Recognition model based on YOLOv10n. To achieve synergistic optimization of modulation recognition performance and model lightweighting, the model structure is systematically improved through targeted strategies, including network channel dimension reduction, core functional module iteration, down-sampling mechanism innovation, and partial convolution replacement. Consequently, core bottlenecks prevalent in existing YOLO-based AMR models—such as parameter redundancy, high computational complexity, and limited adaptability to resource-constrained scenarios like edge nodes and FPGAs—are significantly reduced. Experimental results on the RadioML2016.10a and RadioML2016.10b benchmark datasets demonstrate that PLS-YOLO exhibits superior comprehensive performance. While the integrity of integrated signal classification and localization functions is maintained, both parameter and computational complexity are significantly reduced compared to the baseline YOLOv10n, accompanied by a notable enhancement in recognition accuracy, thereby significantly outperforming mainstream comparative models. In conclusion, the effectiveness and feasibility of the proposed optimization strategies are verified, providing a reliable technical path for the engineering implementation of AMR technology, while the identified potential for improvement in analog modulation signal recognition clarifies specific directions for future research.
- Deep Learning,
- Automatic Modulation Recognition,
- Lightweight,
- YOLOv10

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References(24)

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