Direct Learning Algorithm for Digital Predistortion Training Using Sub-Nyquist Intermediate Frequency Feedback Signal

Abstract

In this paper, a novel direct learning algorithm is proposed to identify the digital predistortion (DPD) coefficients that linearize a power amplifier (PA) using sub-Nyquist sampled intermediate frequency (IF) output of a heterodyne transmitter observation receiver (TOR). The learning algorithm is complemented with a joint time and phase alignment procedure to compensate for the unknown phase of the IF carrier as well as the delay between the PA input and output signals. By sub-Nyquist sampling at IF, the proposed method avoids the need for challenging receiver calibration that compensates for significant IQ imbalance exhibited by direct conversion receivers. Furthermore, it provides a very attractive flexibility in choosing the IF and consequently allows for a high subsampling factor. It is also extended to account for the nonflat frequency response of the TOR, thus avoiding the need for an explicit calibration step. Finally, measurement results were performed to linearize a PA demonstrator driven by a 320-MHz wide carrier aggregated LTE signal centered at 31 GHz using a complexity reduced Volterra-based DPD. Excellent linearization capacity (ACPR of 50 dBc and normalized mean square error of 2%) using significantly low sampling rates (as low as 40 Msps) is reported.