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Noncoherent Detection of Optimal FTN Signals with Differential Encoding

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Internet of Things, Smart Spaces, and Next Generation Networks and Systems (NEW2AN 2020, ruSMART 2020)

Abstract

Noncoherent signal detection has a long history of application in the data transfer systems. Currently, this method is being considered for application in 6G communication standards. This article is devoted to the study of the possibility of joint use of noncoherent detection algorithm and optimal Faster than Nyquist (FTN) signals. The optimal FTN signals are obtained as a solution to the optimization problem in accordance with the criterion of the fixed reduction rate of out-of-band emissions. These signals are characterized by controlled interference in time, which allows you to get the desired level of bit error rate (BER) performance. The article presents the results of simulation modeling of data transmission in the channel with additive white Gaussian noise (AWGN) using the proposed optimal differential FTN (DFTN) signals and noncoherent symbol-by-symbol detection. A similar experimental study based on the software defined radio (SDR) platform was also conducted. The difference between the results of simulation and experiment is determined by the influence of symbol synchronization inaccuracy and is not more than 1 dB.

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Acknowledgements

The results of the work were obtained under the grant of the President of the Russian Federation for state support of young Russian scientists (agreement MK-1571.2019.8 №075-15-2019-1155) and used computational resources of Peter the Great Saint-Petersburg Polytechnic University Supercomputing Center (http://www.scc.spbstu.ru).

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Correspondence to Anna S. Ovsyannikova .

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Makarov, S.B., Ishkaev, I.R., Lavrenyuk, I.I., Ovsyannikova, A.S., Zavjalov, S.V. (2020). Noncoherent Detection of Optimal FTN Signals with Differential Encoding. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2020 2020. Lecture Notes in Computer Science(), vol 12526. Springer, Cham. https://doi.org/10.1007/978-3-030-65729-1_15

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  • DOI: https://doi.org/10.1007/978-3-030-65729-1_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65728-4

  • Online ISBN: 978-3-030-65729-1

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