Improving the Bootstrap of Blind Equalizers with Variational Autoencoders
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Blind Equalization and Channel Estimation in Coherent Optical Communications Using Variational Autoencoders
We investigate the potential of adaptive blind equalizers based on variational inference for carrier recovery in optical communications. These equalizers are based on a low-complexity approximation of maximum likelihood …
Variational InferenceBlind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
We propose a novel frequency-domain blind equalization scheme for coherent optical communications. The method is shown to achieve similar performance to its recently proposed time-domain counterpart with lower computatio…
Blind Channel Equalization using Variational Autoencoders
A new maximum likelihood estimation approach for blind channel equalization, using variational autoencoders (VAEs), is introduced. Significant and consistent improvements in the error rate of the reconstructed symbols, c…
Blind Channel Equalization Using Vector-Quantized Variational Autoencoders
State-of-the-art high-spectral-efficiency communication systems employ high-order modulation formats coupled with high symbol rates to accommodate the ever-growing demand for data rate-hungry applications. However, such …
On the Semi-Blind Mutually Referenced Equalizers for MIMO Systems
Minimizing training overhead in channel estimation is a crucial challenge in wireless communication systems. This paper presents an extension of the traditional blind algorithm, called "Mutually referenced equalizers" (M…