Self-Sensing Digital Predistortion of RF Power Amplifiers for 6G Intelligent Radio

• Published in: IEEE microwave and wireless components letters Vol. 32; no. 5; pp. 475 - 478
• Main Authors: Yu, Yucheng, Cai, Jialin, Zhu, Xiao-Wei, Chen, Peng, Yu, Chao
• Format: Journal Article
• Language: English
• Published: IEEE 01.05.2022
• Subjects: Baseband; Complexity theory; Digital predistortion (DPD); intelligent radio; Mathematical models; neural network; Neural networks; power amplifier (PA); Predistortion; Sensors; Wireless sensor networks
• DOI: 10.1109/LMWC.2021.3139018
• ISSN: 1531-1309

The future intelligent communication systems will dynamically adjust the transmitted signal according to the radio environment and human behavior, which will lead to the rapid change of the characteristics of power amplifier (PA) and bring new challenges for digital predistortion (DPD). In this letter, a novel self-sensing DPD (SS-DPD) technique is proposed to linearize PA driven by fast time-varied signals. By automatically sensing the features of input signal and integrating them into the neural network, the proposed model is capable of linearizing the PA operated in such time-varied scenarios without updating DPD coefficients. Furthermore, the polynomial basis functions are embedded into neural network to reduce the complexity. Experimental results on a Doherty PA driven by the fast time-varied signal show that the proposed method can achieve good performance constantly with low complexity.