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

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 convention...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE International Conference on Communications (2003) s. 6367 - 6372
Hlavní autoři: Feys, Thomas, Mestre, Xavier, Rottenberg, Francois
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 28.05.2023
Témata:
Limiting
linear precoding
Massive MIMO
neural networks
non-linear power amplifier
Nonlinear distortion
Parameter estimation
Power amplifiers
Precoding
Self-supervised learning
Simulation
ISSN:1938-1883
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
ISSN:1938-1883
DOI:10.1109/ICC45041.2023.10279810