Improving the Bootstrap of Blind Equalizers with Variational Autoencoders
arXiv:2301.06576v18 citationsh-index: 32
Originality Synthesis-oriented
AI Analysis
This addresses a domain-specific issue in signal processing for improving equalizer performance, but appears incremental as it builds on existing VAE methods.
The paper tackles the problem of bootstrapping blind equalizers at critical working points by analyzing existing algorithms and demonstrating that variational autoencoder (VAE)-based equalizers can improve this process.
We evaluate the start-up of blind equalizers at critical working points, analyze the advantages and obstacles of commonly-used algorithms, and demonstrate how the recently-proposed variational autoencoder (VAE) based equalizers can improve bootstrapping.