Self-Supervised Learning of Linear Precoders under Non-Linear PA Distortion for Energy-Efficient Massive MIMO Systems

• URL: http://arxiv.org/abs/2210.07037v1
• Date: Thu, 13 Oct 2022 13:48:50 GMT
• Title: Self-Supervised Learning of Linear Precoders under Non-Linear PA Distortion for Energy-Efficient Massive MIMO Systems
• Authors: Thomas Feys, Xavier Mestre, Fran\c{c}ois Rottenberg
• Abstract summary: A massive multiple input multiple output (MIMO) system is 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. 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 presence of this non-linear distortion.
• Score: 9.324642081509756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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