A Digital Predistortion Scheme Exploiting Degrees-of-Freedom for Massive MIMO Systems

TL;DR

This paper introduces a low-order digital predistortion method for massive MIMO systems that leverages degrees-of-freedom to significantly reduce complexity while maintaining performance, enabling faster and more efficient wireless communication.

Contribution

It proposes a novel indirect learning structure that exploits massive DoFs to use lower-order polynomials for DPD in massive MIMO systems, reducing complexity.

Findings

Achieves the same performance with a 3rd order polynomial as conventional 11th order.

Reduces computational complexity by 70%.

Enables ultra-low latency communications.

Abstract

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.