Variational Autoencoders for Studying the Manifold of Precoding Matrices with High Spectral Efficiency
Year: 2021
Type: preprint
Abstract: In multiple-input multiple-output (MIMO) wireless communications systems, neural networks have been employed for channel decoding, detection, channel estimation, and resource management. In this paper, we look at how to use a variational autoencoder to find a precoding matrix with a high Spectral Efficiency (SE). To identify efficient precoding matrices, an optimization approach is used. Our objective is to create a less time-consuming algorithm with minimum quality degradation. To build precoding matrices, we employed two forms of variational autoencoders: conventional variational autoencoders (VAE) and conditional variational autoencoders (CVAE). Both methods may be used to study a wide range of optimal precoding matrices. To the best of our knowledge, the development of precoding matrices for the spectral efficiency objective function (SE) utilising VAE and CVAE methods is being published for the first time.
Source: arXiv (Cornell University)
Authors: Evgeny Bobrov, Alexander Markov, Dmitry Vetrov
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3217435486