A study of traditional and AI-based models for second-order intermodulation product suppression

Degtyarev A.A., Bakholdin N.V., Maslovskiy A.Y., Bakhurin S.A.
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This paper investigates neural network models and polynomial models based on Chebyshev polynomials for interference compensation. It is shown that the neural network model provides compensation for parasitic interference without the need for parameter tuning, unlike the polynomial model, which requires the selection of optimal delays. The L-BFGS method is applied to both architectures, achieving a compensation level comparable to the LS solution for the polynomial model, with an NMSE result of −23.59 dB and requiring fewer than 2000 iterations, confirming its high efficiency. Additionally, due to the strong generalization ability of neural network architectures, the first-order method for neural networks demonstrates faster convergence compared to the polynomial model. In 20 000 iterations, the neural network model achieves a 0.44 dB improvement in compensation level compared to the polynomial model. In contrast, the polynomial model can only achieve high compensation levels with optimal first-order method parameter tuning, highlighting one of the key advantages of neural network models.

Keywords: IMD2, adaptive filter, artificial intelligence, Chebyshev polynomials
Citation in English: Degtyarev A.A., Bakholdin N.V., Maslovskiy A.Y., Bakhurin S.A. A study of traditional and AI-based models for second-order intermodulation product suppression // Computer Research and Modeling, 2024, vol. 16, no. 7, pp. 1569-1578
Citation in English: Degtyarev A.A., Bakholdin N.V., Maslovskiy A.Y., Bakhurin S.A. A study of traditional and AI-based models for second-order intermodulation product suppression // Computer Research and Modeling, 2024, vol. 16, no. 7, pp. 1569-1578
DOI: 10.20537/2076-7633-2024-16-7-1569-1578
URL: http://crm-en.ics.org.ru/journal/article/3554/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

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