Abstract
Least squares (LS) estimation is widely used in model extraction of digital predistortion for RF power amplifiers. In order to reduce computational complexity and implementation cost, it is desirable to use a small number of training samples in the model parameter estimation. However, due to strong correlations between data samples in a real transmit signal, the ill-conditioning problem becomes severe in standard LS, which often leads to large errors occurring in model extraction. Using a short training sequence can also cause mismatch between the statistical properties of the training data and the actual signal that the amplifier transmits, which could degrade the linearization performance of the digital predistorter. In this paper, we propose first to use a 1-bit ridge regression algorithm to eliminate the ill-conditioning problem in the LS estimation and then use root-mean-squares based coefficients weighting and averaging approach to reduce the errors caused by the statistical mismatch. Experimental results show that the proposed approach can produce excellent model extraction accuracy with only a very small number of training samples, which dramatically reduces the computational complexity and the system implementation cost.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IEEE Transactions on Microwave Theory and Techniques
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.