  The primary source of nonlinear distortion in wireless transmitters is the
power amplifier (PA). Conventional digital predistortion (DPD) schemes use
high-order polynomials to accurately approximate and compensate for the
nonlinearity of the PA. This is not practical for scaling to tens or hundreds
of PAs in massive multiple-input multiple-output (MIMO) systems. There is more
than one candidate precoding matrix in a massive MIMO system because of the
excess degrees-of-freedom (DoFs), and each precoding matrix requires a
different DPD polynomial order to compensate for the PA nonlinearity. This
paper proposes a low-order DPD method achieved by exploiting massive DoFs of
next-generation front ends. We propose a novel indirect learning structure
which adapts the channel and PA distortion iteratively by cascading adaptive
zero forcing precoding and DPD. Our solution uses a 3rd order polynomial to
achieve the same performance as the conventional DPD using an 11th order
polynomial for a 100x10 massive MIMO configuration. Experimental results show a
70% reduction in computational complexity, enabling ultra-low latency
communications.
