The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Neural Network Detection of Data Sequences in Communication Systems
Open accessAbstract
We consider detection based on deep learning, and show it is possible to train detectors that perform well without any knowledge of the underlying channel models. Moreover, when the channel model is known, we demonstrate that it is possible to train detectors that do not require channel state information (CSI). In particular, a technique we call a sliding bidirectional recurrent neural network (SBRNN) is proposed for detection where, after training, the detector estimates the data in real time as the signal stream arrives at the receiver. We evaluate this algorithm, as well as other neural network (NN) architectures, using the Poisson channel model, which is applicable to both optical and molecular communication systems. In addition, we also evaluate the performance of this detection method applied to data sent over a molecular communication platform, where the channel model is difficult to model analytically. We show that SBRNN is computationally efficient, and can perform detection under various channel conditions without knowing the underlying channel model. We also demonstrate that the bit error rate performance of the proposed SBRNN detector is better than that of a Viterbi detector with imperfect CSI as well as that of other NN detectors that have been previously proposed. Finally, we show that the SBRNN can perform well in rapidly changing channels, where the coherence time is on the order of a single symbol duration.
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Cites
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Cited by
Deep Learning Enabled Semantic Communication Systems
Xie, H., Qin, Z., Li, G. Y., & Juang, B.-H. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3071210 · Cited by 1,602
A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future
Karabulut Kurt, G., Khoshkholgh, M. G., Alfattani, S., Ibrahim, A., Darwish, T. S. J., Alam, M. S., Yanikomeroglu, H., & Yongacoglu, A. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3066905 · Cited by 538
A Very Brief Introduction to Machine Learning With Applications to Communication Systems
Simeone, O. (2018) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2018.2881442 · Cited by 516
Samuel, N., Diskin, T., & Wiesel, A. (2019) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2019.2899805 · Cited by 515
DeepJSCC-f: Deep Joint Source-Channel Coding of Images With Feedback
Kurka, D. B., & Gunduz, D. (2020) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2020.2987203 · Cited by 470
Metadata
- Authors
- Farsad, N., & Goldsmith, A.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2018-11-01
- Volume
- 66
- Issue
- 21
- Pages
- 5663-5678
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2786361328
- Cited by
- 358
- References
- 13
- FWCI
- 17.08
- Type
- journal article
- Language
- en
- Open access
- green
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