The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors
Open accessAbstract
This paper looks into the modulation classification problem for a distributed wireless spectrum sensing network. First, a new data-driven model for automatic modulation classification based on long short term memory (LSTM) is proposed. The model learns from the time domain amplitude and phase information of the modulation schemes present in the training data without requiring expert features like higher order cyclic moments. Analyses show that the proposed model yields an average classification accuracy of close to 90% at varying signal-to-noise ratio conditions ranging from 0 dB to 20 dB. Further, we explore the utility of this LSTM model for a variable symbol rate scenario. We show that a LSTM based model can learn good representations of variable length time domain sequences, which is useful in classifying modulation signals with different symbol rates. The achieved accuracy of 75% on an input sample length of 64 for which it was not trained, substantiates the representation power of the model. To reduce the data communication overhead from distributed sensors, the feasibility of classification using averaged magnitude spectrum data and on-line classification on the low-cost spectrum sensors are studied. Furthermore, quantized realizations of the proposed models are analyzed for deployment on sensors with low processing power.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
Convolutional Radio Modulation Recognition Networks
O’Shea, T. J., Corgan, J., & Clancy, T. C. (2016) · Communications in Computer and Information Science · doi:10.1007/978-3-319-44188-7_16 · Cited by 1,584
Survey of automatic modulation classification techniques: classical approaches and new trends
Dobre, O. A., Abdi, A., Bar-Ness, Y., & Su, W. (2007) · IET Communications · doi:10.1049/iet-com:20050176 · Cited by 1,393
Visualizing and Understanding Recurrent Networks
Karpathy, A., Johnson, J., & Fei-Fei, L. (2015) · arXiv · doi:10.48550/arxiv.1506.02078 · Cited by 845
Statistical tests for presence of cyclostationarity
Dandawate, A. V., & Giannakis, G. B. (1994) · IEEE Transactions on Signal Processing · doi:10.1109/78.317857 · Cited by 739
Signal interception: a unifying theoretical framework for feature detection
Gardner, W. A. (1988) · IEEE Transactions on Communications · doi:10.1109/26.3769 · Cited by 609
Deep architectures for modulation recognition
West, N. E., & O'Shea, T. (2017) · 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) · doi:10.1109/dyspan.2017.7920754 · Cited by 567
A new approach to signal classification using spectral correlation and neural networks
Fehske, A., Gaeddert, J., & Reed, J. H. (2005) · First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. · doi:10.1109/dyspan.2005.1542629 · Cited by 447
Cited by
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,768
DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive
Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W. (2019) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2019.2952332 · Cited by 610
Peng, S., Sun, S., & Yao, Y.-D. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3085433 · Cited by 308
Automatic modulation classification using recurrent neural networks
Hong, D., Zhang, Z., & Xu, X. (2017) · 2017 3rd IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/compcomm.2017.8322633 · Cited by 257
AvgNet: Adaptive Visibility Graph Neural Network and Its Application in Modulation Classification
Xuan, Q., Zhou, J., Qiu, K., Chen, Z., Xu, D., Zheng, S., & Yang, X. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3146836 · Cited by 106
“[26] proposed a two-layer Long Short Term Memory (LSTM) model for automatic modulation classification, which has considerable accuracy but is relatively time-consuming.”
Background
Metadata
- Authors
- Rajendran, S., Meert, W., Giustiniano, D., Lenders, V., & Pollin, S.
- Venue
- IEEE Transactions on Cognitive Communications and Networking
- Published
- 2018-09
- Volume
- 4
- Issue
- 3
- Pages
- 433-445
- ISSN
- 2332-7731 (ISSN-L)
- 2372-2045
- OpenAlex
- W2741230443
- Cited by
- 868
- References
- 15
- FWCI
- 35.36
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors. https://app.cochat.ai/papers/10.1109/tccn.2018.2835460
- The paper
- Rajendran, S., Meert, W., Giustiniano, D., Lenders, V., & Pollin, S. (2018). Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors. IEEE Transactions on Cognitive Communications and Networking. https://doi.org/10.1109/tccn.2018.2835460
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
