A General Approach to Fully Linearize the Power Amplifiers in mMIMO with Less Complexity

TL;DR

This paper introduces a low-complexity digital predistortion scheme for linearizing multiple power amplifiers in massive MIMO systems, reducing complexity while maintaining performance.

Contribution

It proposes a novel LC-DPD scheme that adaptively linearizes multiple PAs with lower complexity compared to traditional methods.

Findings

LC-DPD achieves effective linearization with less computational complexity.

Algorithms based on correlation improve coefficient adaptation.

Numerical results demonstrate comparable performance to full-featured DPD.

Abstract

A radio frequency (RF) power amplifier (PA) plays an important role to amplify the message signal at higher power to transmit it to a distant receiver. Due to a typical nonlinear behavior of the PA at high power transmission, a digital predistortion (DPD), exploiting the preinversion of the nonlinearity, is used to linearize the PA. However, in a massive MIMO (mMIMO) transmitter, a single DPD is not sufficient to fully linearize the hundreds of PAs. Further, for the full linearization, assigning a separate DPD to each PA is complex and not economical. In this work, we address these challenges via the proposed low-complexity DPD (LC-DPD) scheme. Initially, we describe the fully-featured DPD (FF-DPD) scheme to linearize the multiple PAs and examine its complexity. Thereafter, using it, we derive the LC-DPD scheme that can adaptively linearize the PAs as per the requirement. The coefficients in the two schemes are learned using the algorithms that adopt indirect learning architecture based recursive prediction error method (ILA-RPEM) due to its adaptive and free from matrix inversion operations. Furthermore, for the LC-DPD structure, we have proposed three algorithms based on correlation of its common coefficients with the distinct coefficients. Lastly, the performance of the algorithms are quantified using the obtained numerical results.