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arxiv: 2604.10317 · v1 · submitted 2026-04-11 · 📡 eess.SP

Automatic Modulation Classification via Green Machine Learning

Chee-An Yu , Young-Kai Chen , C.-C. Jay Kuo This is my paper

Pith reviewed 2026-05-10 15:26 UTC · model grok-4.3

classification 📡 eess.SP
keywords automatic modulation classificationgreen machine learninglightweight modelsmulti-domain featuresSNR soft routingedge AIfeature learningconstellation diagrams
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The pith

GAMC classifies modulation types accurately in high noise by transforming I/Q signals into multi-domain features and routing predictions by SNR while using half the parameters of comparable models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents GAMC, a four-stage lightweight method for automatic modulation classification that targets edge devices facing dynamic noisy channels. Raw I/Q signals are first converted into constellation diagrams and spatio-temporal graphs, from which statistical and topological features are extracted. Supervised learning then compresses these into low-dimensional representations, and a context-aware SNR soft routing step ensembles the final predictions. A sympathetic reader would care because the approach claims to cut model size by 50 percent and computation to between 3 and 42 percent of lightweight deep-learning alternatives while preserving or improving accuracy across SNR levels.

Core claim

GAMC mitigates domain shifts caused by high noise through a pipeline that transforms raw received I/Q signals into multi-domain representations, extracts comprehensive statistical and topological features, applies supervised feature learning to produce robust low-dimensional descriptors, and ensembles downstream classifier outputs via a context-aware SNR soft routing mechanism, thereby achieving higher accuracy at substantially lower parameter count and computational cost than existing lightweight deep learning models.

What carries the argument

The four-stage GAMC pipeline with its final context-aware SNR soft routing mechanism that ensembles predictions according to estimated signal quality.

If this is right

  • Reduces model parameters by 50 percent relative to other lightweight approaches.
  • Operates at 3 to 42 percent of the computational cost of lightweight deep learning models.
  • Maintains higher classification accuracy across a range of SNR conditions.
  • Mitigates performance drops from domain shifts induced by high noise.
  • Supports deployment of modulation classification on resource-limited edge hardware.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The multi-domain feature extraction strategy could be adapted to other time-series classification tasks where both statistical summaries and graph-based topology matter.
  • SNR soft routing offers a template for quality-aware ensembling in any setting where input reliability fluctuates, such as sensor fusion or medical signal analysis.
  • The emphasis on parameter and compute reduction suggests the method could be further paired with quantization or pruning for even lower power use on embedded platforms.
  • Validation on measured over-the-air signals rather than simulated channels would test whether the robustness to noise transfers to real hardware impairments.

Load-bearing premise

That converting I/Q signals into multiple domains, extracting statistical and topological features, learning compact representations under label supervision, and routing by SNR will reliably produce discriminative low-dimensional features and accurate ensemble outputs even when noise levels vary widely.

What would settle it

Test GAMC and competing lightweight deep-learning models on the RadioML dataset at SNR values below zero decibels; if GAMC loses its reported accuracy advantage or its computational savings come with unacceptable error rates, the central claim is falsified.

Figures

Figures reproduced from arXiv: 2604.10317 by C.-C. Jay Kuo, Chee-An Yu, Young-Kai Chen.

Figure 1
Figure 1. Figure 1: The block diagram of an adaptive modulation communication system. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed green automatic modulation classification (GAMC) system. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The illustration of the signal time series (left), the constellation map (middle), and the spatio-temporal graph representation (right). [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Feature learning by linear combination of raw features [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Classification Accuracy of Channel Quality Indicator for different [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: Feature Importance Analysis of the learning feature set for the low [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 6
Figure 6. Figure 6: accuracy of different feature sets across different SNRs [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Feature Importance Analysis of the raw feature set for the low (left) [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: accuracy of different numbers of MoE across SNRs [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Confusion Matrices at representative SNRs. The prediction accuracy of w/o MoE, 3-MoEs, and 5-MoEs is from top to bottom. The overall accuracy, [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗
read the original abstract

In this work, we propose an interpretable, robust, and lightweight machine learning method for automatic modulation classification (AMC) under dynamic and noisy channel conditions. It is called green automatic modulation classification (GAMC) and targets edge artificial intelligence (AI) with low computational complexity and a small model size. GAMC operates in four stages. First, raw received I/Q signals are transformed into multi-domain representations, including constellation diagrams and spatio-temporal graphs. Second, we extract a comprehensive set of statistical and topological features from time-series signals, constellation diagrams, and graphs. Third, a supervised feature learning process leverages label guidance to project high-dimensional features into robust, discriminative low-dimensional ones. Finally, a context-aware Signal-to-Noise Ratio (SNR) soft routing mechanism ensembles predictions from downstream classifiers. Experimental results show that GAMC effectively mitigates domain shifts caused by high noise. It strikes a good balance between accuracy and efficiency, reducing the number of model parameters by $50\%$, operating at $3\%$ to $42\%$ of the computational cost of lightweight deep learning models, and maintaining higher accuracy in various SNRs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes Green Automatic Modulation Classification (GAMC), a four-stage lightweight ML pipeline for automatic modulation classification (AMC) under noisy dynamic channels. Raw I/Q signals are transformed into multi-domain representations (constellation diagrams and spatio-temporal graphs); statistical and topological features are extracted; supervised feature learning projects them to low-dimensional discriminative representations; and a context-aware SNR soft-routing mechanism ensembles downstream classifier predictions. The central claims are that GAMC mitigates noise-induced domain shifts while achieving higher accuracy than lightweight deep learning models at substantially lower cost (50% fewer parameters and 3-42% of their computational cost), making it suitable for edge AI.

Significance. If the efficiency and accuracy claims are substantiated with complete experimental protocols, the work could be significant for practical AMC on resource-limited devices. The combination of topological features with SNR-aware routing offers a potentially interpretable alternative to pure deep learning approaches in signal processing.

major comments (3)
  1. [Experimental Results] The efficiency claims (50% parameter reduction and 3-42% computational cost relative to lightweight DL models) are load-bearing for the central contribution, yet the manuscript provides no explicit baseline architectures, no FLOPs/inference-time/energy measurement methodology, and no per-stage cost breakdown. Topological feature extraction (e.g., persistent homology or graph invariants on noisy I/Q-derived graphs) has non-trivial complexity that must be shown to be net cheaper than the baselines.
  2. [Experimental Setup] No information is supplied on the datasets (modulation types, sample counts, SNR ranges), validation splits, error bars, or statistical significance tests. Without these, it is impossible to evaluate whether the reported accuracy gains and robustness to domain shifts are supported by the data.
  3. [Method] The complexity analysis of the topological feature extraction stage is absent. This is required to substantiate that the overall pipeline is lighter than the lightweight CNN baselines it claims to outperform.
minor comments (2)
  1. [Abstract] The abstract refers to 'various SNRs' without specifying the tested range or values.
  2. [Method] The construction of 'spatio-temporal graphs' from I/Q signals should be defined more precisely, including any parameters used for graph formation.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed review. The comments identify key areas where additional clarity and documentation will strengthen the experimental claims. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Experimental Results] The efficiency claims (50% parameter reduction and 3-42% computational cost relative to lightweight DL models) are load-bearing for the central contribution, yet the manuscript provides no explicit baseline architectures, no FLOPs/inference-time/energy measurement methodology, and no per-stage cost breakdown. Topological feature extraction (e.g., persistent homology or graph invariants on noisy I/Q-derived graphs) has non-trivial complexity that must be shown to be net cheaper than the baselines.

    Authors: We agree that the efficiency claims require more explicit supporting documentation. The original manuscript reports aggregate comparisons but does not name the exact baseline models or detail the measurement protocol. In the revision we will add: (1) explicit baseline architectures (a 5-layer 1D-CNN, a lightweight ResNet-18 variant, and MobileNetV2 adapted for I/Q input, with their parameter counts listed); (2) FLOPs and inference-time methodology using the PyTorch thop profiler together with wall-clock timing on an NVIDIA Jetson Nano edge platform; and (3) a per-stage breakdown table. For topological feature extraction we will include both asymptotic analysis (graph construction O(M^2) with M fixed at 100 points, persistent homology via optimized Ripser library) and empirical timing showing the stage contributes <8 % of total inference cost, thereby confirming the reported 3-42 % overall compute reduction. revision: yes

  2. Referee: [Experimental Setup] No information is supplied on the datasets (modulation types, sample counts, SNR ranges), validation splits, error bars, or statistical significance tests. Without these, it is impossible to evaluate whether the reported accuracy gains and robustness to domain shifts are supported by the data.

    Authors: We will add a comprehensive Experimental Setup subsection. It will specify the RadioML 2016.10a and 2018.01a datasets (11 modulation formats: BPSK, QPSK, 8PSK, 16QAM, 64QAM, etc.), total sample count (~220 k), SNR range (-20 dB to +30 dB in 2 dB steps), train/validation/test splits (70/15/15), and reporting of mean accuracy ± standard deviation over five independent runs. We will also include paired statistical significance tests (McNemar’s test) between GAMC and each baseline to substantiate the accuracy and robustness claims. revision: yes

  3. Referee: [Method] The complexity analysis of the topological feature extraction stage is absent. This is required to substantiate that the overall pipeline is lighter than the lightweight CNN baselines it claims to outperform.

    Authors: We acknowledge the absence of a dedicated complexity subsection. The revised manuscript will add this analysis under the Method section. Graph construction from constellation points uses a fixed node count (M = 100) yielding O(M^2) time that is negligible in practice; persistent homology is performed with the Ripser library whose practical complexity is linear in the number of simplices for small filtrations. Combined empirical measurements on the target edge hardware will be reported to show that the topological stage accounts for a small fraction of total latency, preserving the overall efficiency advantage over the CNN baselines. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical ML pipeline with no derivational reductions

full rationale

The paper describes an empirical four-stage machine learning pipeline for AMC (multi-domain signal transformation, statistical/topological feature extraction, supervised projection, and SNR soft routing) and reports experimental accuracy/efficiency results. No mathematical derivations, equations, or first-principles predictions appear in the abstract or description. Claims of 50% parameter reduction and 3-42% computational cost are presented as measured outcomes against unspecified lightweight DL baselines rather than as outputs forced by fitted inputs or self-citations. No self-definitional steps, uniqueness theorems, or ansatz smuggling via citation are present. The work is self-contained as a described method plus empirical validation, with no load-bearing argument reducing to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review is limited to the abstract; no free parameters, axioms, or invented entities are identifiable or extractable from the provided text.

pith-pipeline@v0.9.0 · 5496 in / 1299 out tokens · 78282 ms · 2026-05-10T15:26:01.225088+00:00 · methodology

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Reference graph

Works this paper leans on

47 extracted references · 47 canonical work pages

  1. [1]

    Recent advances in automatic modulation classification technology: Methods, results, and prospects,

    Q. Zheng, X. Tian, L. Yu, A. Elhanashi, and S. Saponara, “Recent advances in automatic modulation classification technology: Methods, results, and prospects,”International Journal of Intelligent Systems, vol. 2025, no. 1, p. 4067323, 2025

    work page 2025

  2. [2]

    Automatic modulation classification of digital communication signals using svm based on hybrid features, cyclostationary, and information entropy,

    Y . Wei, S. Fang, and X. Wang, “Automatic modulation classification of digital communication signals using svm based on hybrid features, cyclostationary, and information entropy,”Entropy, vol. 21, no. 8, p. 745, 2019

    work page 2019

  3. [3]

    Maximum-likelihood classification for digital amplitude-phase modulations,

    W. Wei and J. M. Mendel, “Maximum-likelihood classification for digital amplitude-phase modulations,”IEEE transactions on Commu- nications, vol. 48, no. 2, pp. 189–193, 2000

    work page 2000

  4. [4]

    Multitask-learning- based deep neural network for automatic modulation classification,

    S. Chang, S. Huang, R. Zhang, Z. Feng, and L. Liu, “Multitask-learning- based deep neural network for automatic modulation classification,” IEEE internet of things journal, vol. 9, no. 3, pp. 2192–2206, 2021

    work page 2021

  5. [5]

    Maximum-likelihood modulation classification for psk/qam,

    J. A. Sills, “Maximum-likelihood modulation classification for psk/qam,” inMILCOM 1999. IEEE Military Communications. Conference Pro- ceedings (Cat. No. 99CH36341), vol. 1. IEEE, 1999, pp. 217–220

    work page 1999

  6. [6]

    Accuracy analysis of feature- based automatic modulation classification via deep neural network,

    Z. Ge, H. Jiang, Y . Guo, and J. Zhou, “Accuracy analysis of feature- based automatic modulation classification via deep neural network,” Sensors, vol. 21, no. 24, p. 8252, 2021

    work page 2021

  7. [7]

    Automatic modulation classification using moments and likelihood maximization,

    M. Abu-Romoh, A. Aboutaleb, and Z. Rezki, “Automatic modulation classification using moments and likelihood maximization,”IEEE Com- munications Letters, vol. 22, no. 5, pp. 938–941, 2018

    work page 2018

  8. [8]

    Novel automatic modulation classification using cumulant features for communications via multipath channels,

    H.-C. Wu, M. Saquib, and Z. Yun, “Novel automatic modulation classification using cumulant features for communications via multipath channels,”IEEE Transactions on Wireless Communications, vol. 7, no. 8, pp. 3098–3105, 2008

    work page 2008

  9. [9]

    Automatic modulation classification for cognitive radios using cyclic feature detection,

    B. Ramkumar, “Automatic modulation classification for cognitive radios using cyclic feature detection,”IEEE Circuits and Systems Magazine, vol. 9, no. 2, pp. 27–45, 2009

    work page 2009

  10. [10]

    Automatic mod- ulation classification using convolutional neural network with features fusion of spwvd and bjd,

    Z. Zhang, C. Wang, C. Gan, S. Sun, and M. Wang, “Automatic mod- ulation classification using convolutional neural network with features fusion of spwvd and bjd,”IEEE Transactions on Signal and Information Processing over Networks, vol. 5, no. 3, pp. 469–478, 2019

    work page 2019

  11. [11]

    Graphic constellations and dbn based automatic modulation classification,

    F. Wang, Y . Wang, and X. Chen, “Graphic constellations and dbn based automatic modulation classification,” in2017 IEEE 85th vehicular technology conference (VTC Spring). IEEE, 2017, pp. 1–5

    work page 2017

  12. [12]

    Genetic algorithm optimized distribution sampling test for m-qam modulation classification,

    Z. Zhu, M. W. Aslam, and A. K. Nandi, “Genetic algorithm optimized distribution sampling test for m-qam modulation classification,”Signal Processing, vol. 94, pp. 264–277, 2014

    work page 2014

  13. [13]

    Fast and robust modulation classification via kolmogorov-smirnov test,

    F. Wang and X. Wang, “Fast and robust modulation classification via kolmogorov-smirnov test,”IEEE Transactions on Communications, vol. 58, no. 8, pp. 2324–2332, 2010

    work page 2010

  14. [14]

    Over-the-air deep learning based radio signal classification,

    T. J. O’Shea, T. Roy, and T. C. Clancy, “Over-the-air deep learning based radio signal classification,”IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 168–179, 2018

    work page 2018

  15. [15]

    Mobilerat: a lightweight radio transformer method for automatic modulation classification in drone communication systems,

    Q. Zheng, X. Tian, Z. Yu, Y . Ding, A. Elhanashi, S. Saponara, and K. Kpalma, “Mobilerat: a lightweight radio transformer method for automatic modulation classification in drone communication systems,” Drones, vol. 7, no. 10, p. 596, 2023

    work page 2023

  16. [16]

    Enhancing automatic modulation recognition with a reconstruction-driven vision transformer under limited labels,

    H. Ahmadi, B. Saffari, S. E. Mahdimahalleh, M. E. Safari, and A. Ahmadi, “Enhancing automatic modulation recognition with a reconstruction-driven vision transformer under limited labels,”arXiv preprint arXiv:2508.20193, 2025

    work page arXiv 2025

  17. [17]

    G-amc: A green automatic modulation classification method,

    C.-A. Yu, Y .-K. Chen, and C.-C. J. Kuo, “G-amc: A green automatic modulation classification method,” 2025, accepted to IEEE GLOBE- COM 2025 Workshop

    work page 2025

  18. [18]

    Performance evaluation of feature-based automatic modulation classification,

    P. Ghasemzadeh, S. Banerjee, M. Hempel, and H. Sharif, “Performance evaluation of feature-based automatic modulation classification,” in2018 12th international conference on signal processing and communication systems (ICSPCS). IEEE, 2018, pp. 1–5

    work page 2018

  19. [19]

    Modulation classification in multipath fading channels using sixth-order cumulants and stacked convolutional auto-encoders,

    Z. Zhang, Z. Hua, and Y . Liu, “Modulation classification in multipath fading channels using sixth-order cumulants and stacked convolutional auto-encoders,”IET communications, vol. 11, no. 6, pp. 910–915, 2017

    work page 2017

  20. [20]

    Deep neural network architectures for modulation classification,

    X. Liu, D. Yang, and A. El Gamal, “Deep neural network architectures for modulation classification,” in2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017, pp. 915–919

    work page 2017

  21. [21]

    Deep learning models for wireless signal classification with distributed low- cost spectrum sensors,

    S. Rajendran, W. Meert, D. Giustiniano, V . Lenders, and S. Pollin, “Deep learning models for wireless signal classification with distributed low- cost spectrum sensors,”IEEE Transactions on Cognitive Communica- tions and Networking, vol. 4, no. 3, pp. 433–445, 2018

    work page 2018

  22. [22]

    Radio modulation classification using deep learning architectures,

    K. Pijackova and T. Gotthans, “Radio modulation classification using deep learning architectures,” in2021 31st international conference radioelektronika (radioelektronika). IEEE, 2021, pp. 1–5

    work page 2021

  23. [23]

    Intra-pulse modulation radar signal recognition based on cldn network,

    S. Wei, Q. Qu, H. Su, M. Wang, J. Shi, and X. Hao, “Intra-pulse modulation radar signal recognition based on cldn network,”IET Radar, Sonar & Navigation, vol. 14, no. 6, pp. 803–810, 2020

    work page 2020

  24. [24]

    Deep multi-scale representation learning with attention for automatic modulation classification,

    X. Wu, S. Wei, and Y . Zhou, “Deep multi-scale representation learning with attention for automatic modulation classification,” in2022 Inter- national Joint Conference on Neural Networks (IJCNN). IEEE, 2022, pp. 1–8

    work page 2022

  25. [25]

    Automatic modulation classification with deep neural networks,

    C. A. Harper, M. A. Thornton, and E. C. Larson, “Automatic modulation classification with deep neural networks,”Electronics, vol. 12, no. 18, p. 3962, 2023

    work page 2023

  26. [26]

    Spectrum interference- based two-level data augmentation method in deep learning for auto- matic modulation classification,

    Q. Zheng, P. Zhao, Y . Li, H. Wang, and Y . Yang, “Spectrum interference- based two-level data augmentation method in deep learning for auto- matic modulation classification,”Neural Computing and Applications, vol. 33, no. 13, pp. 7723–7745, 2021

    work page 2021

  27. [27]

    Dl-pr: Generalized automatic modulation classification method based on deep learning with priori regularization,

    Q. Zheng, X. Tian, Z. Yu, H. Wang, A. Elhanashi, and S. Saponara, “Dl-pr: Generalized automatic modulation classification method based on deep learning with priori regularization,”Engineering Applications of Artificial Intelligence, vol. 122, p. 106082, 2023

    work page 2023

  28. [28]

    Efficient automatic modulation classification for next-generation wireless networks,

    T. T. An, A. Argyriou, A. A. Puspitasari, S. L. Cotton, and B. M. Lee, “Efficient automatic modulation classification for next-generation wireless networks,”IEEE Transactions on Green Communications and Networking, 2025

    work page 2025

  29. [29]

    Kadnet: Low snr automatic modulation classification via snr aware deformable convolu- tion and kolmogorov–arnold networks,

    R. Wang, J. Li, Y . Yang, S. Wang, and B. Zheng, “Kadnet: Low snr automatic modulation classification via snr aware deformable convolu- tion and kolmogorov–arnold networks,”Digital Signal Processing, p. 105942, 2026

    work page 2026

  30. [30]

    Hfdnn: A hybrid fusion deep neural network for robust automatic mod- ulation classification in adverse wireless environments,

    M. M. Tahir, A. Latif, M. S. Younis, R. N. B. Rais, and K. Ammar, “Hfdnn: A hybrid fusion deep neural network for robust automatic mod- ulation classification in adverse wireless environments,”IEEE Access, 2026

    work page 2026

  31. [31]

    Mcformer: A transformer based deep neural network for automatic modulation classification,

    S. Hamidi-Rad and S. Jain, “Mcformer: A transformer based deep neural network for automatic modulation classification,” in2021 IEEE Global Communications Conference (GLOBECOM). IEEE, 2021, pp. 1–6

    work page 2021

  32. [32]

    Fine-grained modulation classification using multi-scale radio trans- former with dual-channel representation,

    Q. Zheng, P. Zhao, H. Wang, A. Elhanashi, and S. Saponara, “Fine-grained modulation classification using multi-scale radio trans- former with dual-channel representation,”IEEE Communications Let- ters, vol. 26, no. 6, pp. 1298–1302, 2022

    work page 2022

  33. [33]

    Moe-amc: Enhancing automatic modula- tion classification performance using mixture-of-experts,

    J. Gao, Q. Cao, and Y . Chen, “Moe-amc: Enhancing automatic modula- tion classification performance using mixture-of-experts,”arXiv preprint arXiv:2312.02298, 2023

    work page arXiv 2023

  34. [34]

    Adversarial attack and reliable defense based on frequency domain feature enhance- ment for automatic modulation classification,

    Y . Meng, P. Qi, S. Zheng, Z. Cai, X. Zhou, and T. Jiang, “Adversarial attack and reliable defense based on frequency domain feature enhance- ment for automatic modulation classification,”IEEE Transactions on Information Forensics and Security, 2025

    work page 2025

  35. [35]

    Attention-enhanced hybrid automatic modulation classification for advanced wireless communication systems: A deep learning-transformer framework,

    S. Ansari, K. A. Alnajjar, S. Majzoub, E. Almajali, A. Jarndal, T. Bonny, A. Hussain, and S. Mahmoud, “Attention-enhanced hybrid automatic modulation classification for advanced wireless communication systems: A deep learning-transformer framework,”IEEE Access, 2025

    work page 2025

  36. [36]

    Avgnet: Adaptive visibility graph neural network and its application in modulation classification,

    Q. Xuan, J. Zhou, K. Qiu, Z. Chen, D. Xu, S. Zheng, and X. Yang, “Avgnet: Adaptive visibility graph neural network and its application in modulation classification,”IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 1516–1526, 2022

    work page 2022

  37. [37]

    Automatic mod- ulation classification based on cnn-transformer graph neural network,

    D. Wang, M. Lin, X. Zhang, Y . Huang, and Y . Zhu, “Automatic mod- ulation classification based on cnn-transformer graph neural network,” Sensors, vol. 23, no. 16, p. 7281, 2023

    work page 2023

  38. [38]

    Deep learning-based robust automatic modulation classification for cognitive radio networks,

    S.-H. Kim, J.-W. Kim, W.-P. Nwadiugwu, and D.-S. Kim, “Deep learning-based robust automatic modulation classification for cognitive radio networks,”IEEE access, vol. 9, pp. 92 386–92 393, 2021

    work page 2021

  39. [39]

    Xgboost: A scalable tree boosting system,

    T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” inProceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785–794

    work page 2016

  40. [40]

    A statistics- based feature generation (sfg) method: Theory and applications,

    X. Wang, Y . Wu, H. Li, V . K. Mishra, and C.-C. J. Kuo, “A statistics- based feature generation (sfg) method: Theory and applications,” in2024 IEEE International Conference on Big Data (BigData). IEEE, 2024, pp. 5731–5738

    work page 2024

  41. [41]

    Radioml2016.10a/10b/10c,

    T. O’Shea, “Radioml2016.10a/10b/10c,” 2025. [Online]. Available: https://dx.doi.org/10.21227/wz93-h307

    work page doi:10.21227/wz93-h307 2025

  42. [42]

    A spatiotemporal multi-channel learning framework for automatic modulation recognition,

    J. Xu, C. Luo, G. Parr, and Y . Luo, “A spatiotemporal multi-channel learning framework for automatic modulation recognition,”IEEE Wire- less Communications Letters, vol. 9, no. 10, pp. 1629–1632, 2020

    work page 2020

  43. [43]

    An efficient deep learning model for automatic modulation recognition based on parameter estimation and transformation,

    F. Zhang, C. Luo, J. Xu, and Y . Luo, “An efficient deep learning model for automatic modulation recognition based on parameter estimation and transformation,”IEEE Communications Letters, vol. 25, no. 10, pp. 3287–3290, 2021. 13

    work page 2021

  44. [44]

    A family of automatic modulation classification models based on domain knowledge for various platforms,

    S. Wei, Z. Wang, Z. Sun, F. Liao, Z. Li, L. Zou, and H. Mi, “A family of automatic modulation classification models based on domain knowledge for various platforms,”Electronics, vol. 12, no. 8, p. 1820, 2023

    work page 2023

  45. [45]

    Mobileamct: A lightweight mobile automatic modulation classification transformer in drone communication systems,

    H. Fei, B. Wang, H. Wang, M. Fang, N. Wang, X. Ran, Y . Liu, and M. Qi, “Mobileamct: A lightweight mobile automatic modulation classification transformer in drone communication systems,”Drones, vol. 8, no. 8, p. 357, 2024

    work page 2024

  46. [46]

    Abftnet: An efficient transformer network with alignment before fusion for multimodal automatic modulation recognition,

    M. Ning, F. Zhou, W. Wang, S. Wang, P. Zhang, and J. Wang, “Abftnet: An efficient transformer network with alignment before fusion for multimodal automatic modulation recognition,”Electronics, vol. 13, no. 18, p. 3725, 2024

    work page 2024

  47. [47]

    Gignet: A graph- in-graph neural network for automatic modulation recognition,

    Y . Ke, W. Zhang, Y . Zhang, H. Zhao, and Z. Fei, “Gignet: A graph- in-graph neural network for automatic modulation recognition,”IEEE Transactions on Vehicular Technology, 2025

    work page 2025