  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.
