Self-Supervised Learning of Linear Precoders under Non-Linear PA Distortion for Energy-Efficient Massive MIMO Systems
This work addresses energy efficiency in massive MIMO systems for telecommunications, but it is incremental as it applies an existing neural network approach to a specific distortion model.
The paper tackles the problem of non-linear power amplifier distortion in massive MIMO systems, which limits performance when using conventional precoders, by proposing a neural network to learn precoders that maximize sum rate, achieving a significant increase in energy efficiency compared to conventional methods and perfect digital pre-distortion in the saturation regime.
Massive multiple input multiple output (MIMO) systems are typically designed under the assumption of linear power amplifiers (PAs). However, PAs are typically most energy-efficient when operating close to their saturation point, where they cause non-linear distortion. Moreover, when using conventional precoders, this distortion coherently combines at the user locations, limiting performance. As such, when designing an energy-efficient massive MIMO system, this distortion has to be managed. In this work, we propose the use of a neural network (NN) to learn the mapping between the channel matrix and the precoding matrix, which maximizes the sum rate in the presence of this non-linear distortion. This is done for a third-order polynomial PA model for both the single and multi-user case. By learning this mapping a significant increase in energy efficiency is achieved as compared to conventional precoders and even as compared to perfect digital pre-distortion (DPD), in the saturation regime.