Simplified Real-Valued Time-Delay Neural Network for Compensation of Power Amplifier Impairments
Bulusu, S. S. Krishna Chaitanya; Silva, Lesthuruge; Khan, Bilal; Susarla, Praneeth; Tervo, Nuutti; Sillanpää, Mikko. J.; Silvén, Olli; Leinonen, Marko E.; Juntti, Markku; Pärssinen, Aarno (2025-03-04)
IEEE
04.03.2025
S. S. K. C. Bulusu et al., "Simplified Real-Valued Time-Delay Neural Network for Compensation of Power Amplifier Impairments," 2025 IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless Applications (PAWR), San Juan, PR, USA, 2025, pp. 71-74, doi: 10.1109/PAWR63954.2025.10904050
https://rightsstatements.org/vocab/InC/1.0/
© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202503192108
https://urn.fi/URN:NBN:fi:oulu-202503192108
Tiivistelmä
Abstract
This paper proposes using a cascade of a static nonlinear system and a neural network for power amplifier behavioral modeling and compensation. The results demonstrate that the proposed model provides a better performance-to-complexity trade-off than the current state-of-the-art augmented real-valued time-delay neural network (ARVTDNN) method. When tested with a 64 quadrature amplitude modulated long-term evolution signal with 20 MHz bandwidth and 10.5 dB peak-to-average power ratio at 9.4 dB output back-off on a Doherty-like power amplifier operated at 2.35 GHz, the proposed model achieves an error vector magnitude (EVM) 36.1 dB and adjacent channel leakage ratio (ACLR) of -45.1 dBc. Compared to ARVTDNN, it improves EVM by 2.2 dB and ACLR by 0.4 dB, with a 22% reduction in running complexity.
This paper proposes using a cascade of a static nonlinear system and a neural network for power amplifier behavioral modeling and compensation. The results demonstrate that the proposed model provides a better performance-to-complexity trade-off than the current state-of-the-art augmented real-valued time-delay neural network (ARVTDNN) method. When tested with a 64 quadrature amplitude modulated long-term evolution signal with 20 MHz bandwidth and 10.5 dB peak-to-average power ratio at 9.4 dB output back-off on a Doherty-like power amplifier operated at 2.35 GHz, the proposed model achieves an error vector magnitude (EVM) 36.1 dB and adjacent channel leakage ratio (ACLR) of -45.1 dBc. Compared to ARVTDNN, it improves EVM by 2.2 dB and ACLR by 0.4 dB, with a 22% reduction in running complexity.
Kokoelmat
- Avoin saatavuus [38824]