Abstract
Linearly efficient RF power amplifiers have a tremendous role in wireless communication and radar systems as they lie at the front end of most RF systems. In today’s world of wireless communication, it is not an easy task to design a RF power amplifier that is linearly efficient. There are two main key challenges that one face for making RF power amplifier’s behavior linearly efficient. First is to characterize RF power amplifier’s coefficients smartly. Second is to propose an approach that works on input signal and make its behavior inverse to that of the designed amplifier behavior so that overall response of the system becomes linear. For countering first challenge, most advanced universally accepted algorithms like Memory Polynomial, Generalized Hammerstein, Cross-term Memory Polynomial and Cross-term Hammerstein are implemented to design RF power amplifier models. For countering second challenge, latest DPD algorithms are implemented which make net response of a system linear. The memory models for modelling RF power amplifier are categorized for narrowband and wideband applications. The narrowband power amplifier models include Memory Polynomial and Cross-term Memory Polynomial models whereas wideband power amplifier models include Generalized Hammerstein and Cross-term Hammerstein models. In this paper, various performance indicators like Standard Deviation (SD), Third Order Intercept (TOI), Intermodulation Distortion Products (IMD3), Modulation Error Ratio (MER), Spurious Free Dynamic Range (SFDR) and Error Vector Magnitude (EVM) are used to characterize RF power amplifier for both narrow and wide band applications. The simulation results show that under narrowband applications, Cross-term Memory Polynomial model works best as it has least standard deviation and is also satisfying other performance parameters up to appreciable level with and without DPD algorithm implementation. While for wideband applications, Cross-term Hammerstein model satisfies the performance measuring parameters excellently.
Highlights
There is a compromise between linearity and efficiency when we are talking about Radio Frequency (RF) power amplifier’s response
Only memory polynomial model with or without DPD algorithm was implemented by elaborating its performance on parameters like Third Order Intercept (TOI), Error Vector Magnitude (EVM), Modulation Error Ratio (MER), Adjacent Channel Power Ratio (ACPR) in [5]
The RF power amplifier modelling is done by using actual component characteristics
Summary
There is a compromise between linearity and efficiency when we are talking about RF power amplifier’s response. Only memory polynomial model with or without DPD algorithm was implemented by elaborating its performance on parameters like TOI, EVM, MER, ACPR in [5]. A DPD approach modeled on the basis of indirect learning architecture implemented by a recursive predictive error method (RPEM) was discussed in [8]. Gozde Erdogdu in [9] proposed different DPD algorithms, almost all of them were based on above discussed characterization models for RF power amplifiers and elaborated their performance considering the parameters ACPR, IMD3, SFDR and others. An adaptive basis direct learning algorithm was proposed in [18] for the linearization of power amplifiers having improved normalized mean square error and adjacent channel power ratios than the conventional direct learning method.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Quaid-e-Awam University Research Journal of Engineering Science & Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
