Abstract:
In this paper, we test the performance of some modern stochastic optimization methods and practices with respect to the digital pre-distortion problem, which is a valuable part of processing signal on base stations providing wireless communication. In the first part of our study, we focus on the search for the best performing method and its proper modifications. In the second part, we propose the new, quasi-online, testing framework that allows us to fit our modeling results with the behavior of real-life DPD prototype, retest some selected of practices considered in the previous section and approve the advantages of the method appearing to be the best under real-life conditions. For the used model, the maximum achieved improvement in depth is 7 % in the standard regime and 5 % in the online regime (metric itself is of logarithmic scale). We also achieve a halving of the working time preserving 3 % and 6 % improvement in depth for the standard and online regime, respectively. All comparisons are made to the Adam method, which was highlighted as the best stochastic method for DPD problem in [Pasechnyuk et al., 2021], and to the Adamax method, which is the best in the proposed online regime.
Keywords:
digital pre-distortion, signal processing, stochastic optimization, online learning.
The research of D. Pasechnyuk was supported by the program «Leading Scientific Schools» (grant No. NSh-775.2022.1.1). The research of A. Raigorodskii was supported by Russian Science Foundation grant (project No. 21-71-30005).
Received: 15.01.2022 Accepted: 13.02.2022
Bibliographic databases:
Document Type:
Article
UDC:519.856
Language: English
Citation:
A. V. Alpatov, E. A. Peters, D. A. Pasechnyuk, A. M. Raigorodskii, “Stochastic optimization in digital pre-distortion of the signal”, Computer Research and Modeling, 14:2 (2022), 399–416
\Bibitem{AlpPetPas22}
\by A.~V.~Alpatov, E.~A.~Peters, D.~A.~Pasechnyuk, A.~M.~Raigorodskii
\paper Stochastic optimization in digital pre-distortion of the signal
\jour Computer Research and Modeling
\yr 2022
\vol 14
\issue 2
\pages 399--416
\mathnet{http://mi.mathnet.ru/crm975}
\crossref{https://doi.org/10.20537/2076-7633-2022-14-2-399-416}
\mathscinet{http://mathscinet.ams.org/mathscinet-getitem?mr=4392447}
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https://www.mathnet.ru/eng/crm975
https://www.mathnet.ru/eng/crm/v14/i2/p399
This publication is cited in the following 2 articles:
Minte Zhang, Tong Guo, Guodong Zhang, Zhongxiang Liu, Weijie Xu, “Physics-informed deep learning for structural vibration identification and its application on a benchmark structure”, Phil. Trans. R. Soc. A., 382:2264 (2024)
Madhava Gaikwad, Ashwini Doke, 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), 2023, 1193
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