Abstract
Optimal signals, the shape of which is the result of solving an optimization problem, may provide high spectral efficiency without significant energy losses. The parameters of optimization constraints such as the value of the correlation coefficient and the number of signals considered in the constraint on bit error rate (BER) performance define the shape of the energy spectrum of signals and their correlation properties. In this work sensitivity of energy spectrum and BER performance to variation of parameters of the constraint on correlation coefficient during optimization of FTN pulse shape according to the criterion of maximum energy concentration within occupied frequency bandwidth is estimated. It is shown that the lower the correlation coefficient, the less the energy spectrum changes when the number of signals considered in the constraint on BER performance varies. At the same time, for a rather high value of correlation coefficient increasing the number of signals considered in the constraint on BER performance leads to widening of occupied frequency bandwidth by 7% and reducing the level of emissions by 40 dB at a fixed frequency offset. Besides, energy gain may reach 7 dB. The results obtained during this research allow numerically estimating the possibilities of achieving high spectral and energy characteristics at transmission rates higher than the Nyquist limit.
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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 MК-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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Ovsyannikova, A.S., Zavjalov, S.V., Makarov, S.B., Lavrenyuk, I.I., Wei, X. (2020). Sensitivity of Energy Spectrum Shape and BER to Variation of Parameters Used in Constraint on Correlation Coefficient During FTN Pulse Optimization. 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 12525. Springer, Cham. https://doi.org/10.1007/978-3-030-65726-0_40
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DOI: https://doi.org/10.1007/978-3-030-65726-0_40
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