Abstract
The paper presents a research of the effectiveness of using neural networks to demodulate SEFDM signals when passing through a channel with AWGN. Investigation of the questions of numbers of subcarriers for structure for analogue full search, realization the architecture, of analogue full search for processing SEFDM signals with different bandwidth compression factor between subcarriers are considered. Simulation modeling and comparison with element-by-element signal demodulation algorithms are carried out. Also, considered comparison with analogue full search, which realized in another work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Makarov, S.B., et al.: Optimizing the shape of Faster-Than-Nyquist (FTN) signals with the constraint on energy concentration in the occupied frequency bandwidth. IEEE Access 8, 130082–130093 (2020)
Makarov, S.B., Markov, A.M.: Incoherent reception “in a whole” of spectrally effective signals formed on the basis of phase pulses of the shape sinpx. Radioeng. 84(12) (24), 43–51 (2020)
Irfan, M., Aïssa, S.: Generalization of index-modulation: breaking the conventional limits on spectral and energy efficiencies. In: IEEE Transactions on Wireless Communications, pp.1–1 (2021)
Sadovaya, Y., Gelgor, A.: Synthesis of signals with a low-level of out-of-band emission and peak-to-average power ratio. In: 2018 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), pp. 103–106, St. Petersburg (2018)
Slyusar, V.I., Vasil’ev, K.A.: Potential limits of frequency division multiplexing of N-OFDM signals based on Hartley’s basis functions. Radioelectron. Commun. Syst. 51(3), 129–133 (2008)
Slyusar, V.I., Smolyar, V.G.: Communication channels frequency multiplexing on the basis of superrayleigh signals resolution. Izvestiya Vysshikh Uchebnykh Zavedenij. Radioelektronika 46(7), 30–39 (2003)
Darwazeh, I., Ghannam, H., Xu, T.: The first 15 years of SEFDM: a brief survey. In: 2018 11th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), pp. 1–7, Budapest (2018)
Kanaras, I., Chorti, A., Rodrigues, M., Darwazeh, I.: Analysis of sub-optimum detection techniques for bandwidth efficient multi-carrier communication system. In: Cranfield Multi-Strand Conference: Creating Wealth Through Research and Innovation, pp. 505–510 (2008)
Kislitsyn, A., Krylov, A., Rashich, A.: Experimental evaluation of SEFDM trellis based demodulator. In: 2020 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), pp. 110–113, St. Petersburg, (2020)
Gelgor, A., Gorlov, A., Nguyen, V.P.: Performance analysis of SEFDM with optimal subcarriers spectrum shapes. In: 2017 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Istanbul, pp. 1–5 (2017)
Xu, T., Darwazeh, I.: Design and prototyping of neural network compression for non-orthogonal IoT signals. In: 2019 IEEE Wireless Communications and Networking Conference (WCNC), Marrakesh, pp. 1–6 (2019)
Xu, T., Xu, T., Darwazeh, I.: Deep learning for interference cancellation in non-orthogonal signal based optical communication systems. In: 2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama), pp. 241–248, Toyama (2018)
Haykin, S.: Neural networks and learning machines. Pearson; 3rd edition, p. 936 (2008)
Jia, M., Yin, Z., Guo, Q., Liu, G., Gu, X.: Downlink design for spectrum efficient IoT network. IEEE Internet of Things J. 5(5), 3397–3404 (2018)
Sun, Y., Wang, C., Cai, H., Zhao, C., Wu, Y., Chen, Y.: Deep learning based equalizer for MIMO-OFDM systems with insufficient cyclic prefix. In: 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), pp. 1–5 (2020)
O’Shea, T., Hoydis, J.: An introduction to deep learning for the physical layer. In: IEEE Transactions on Cognitive Communications and Networking, pp. 563–575 (2017)
Xu, T., Darwazeh I.: Deep learning for over-the-air non-orthogonal signal classification. In: 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), pp. 1–5 (2020)
Kislitsyn, A.B.: Algorithms for receiving spectral-effective multi-frequency signals with non-orthogonal frequency compaction: dissertation of the Candidate of Technical Sciences, St. Petersburg, p. 143 (2016)
Acknowledgements
The results of the work were obtained with the support of the scholarship of the President of the Russian Federation to young scientists and graduate students carrying out promising research and development in priority areas of modernization of the Russian economy for 2021–2023 (CП-1671.2021.3) and used computational re-sources of Peter the Great Saint-Petersburg Polytechnic University Supercomputing Center (http://www.scc.spbstu.ru).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Semenova, A.I., Zavjalov, S.V. (2022). Application of Neural Network to Demodulate SEFDM Signals. In: Koucheryavy, Y., Balandin, S., Andreev, S. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2021 2021. Lecture Notes in Computer Science(), vol 13158. Springer, Cham. https://doi.org/10.1007/978-3-030-97777-1_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-97777-1_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-97776-4
Online ISBN: 978-3-030-97777-1
eBook Packages: Computer ScienceComputer Science (R0)