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.

翻译：射频功率放大器在远距离通信中扮演着将信号高功率放大的重要角色。由于高功率传输时功率放大器呈现典型的非线性特性，通常采用数字预失真技术（基于非线性的预逆变换）来实现线性化。然而在大规模MIMO发射机中，单个DPD不足以完全线性化数百个功率放大器。进一步而言，为每个PA单独配置DPD实现全线性化既复杂又不经济。本研究通过提出的低复杂度DPD方案解决了这些挑战。首先描述用于多PA线性化的全功能DPD方案并分析其复杂度，进而推导出可自适应线性化PA的LC-DPD方案。两种方案的系数采用基于间接学习架构的递归预测误差方法进行学习，该方法具有自适应且免矩阵求逆运算的优势。针对LC-DPD结构，我们提出了三种基于公共系数与差异化系数相关性的算法。最后通过数值结果量化评估了各算法的性能。