.png){kind=icon}
返回
Unsupervised Linear and Nonlinear Channel Equalization and Decoding Using Variational Autoencoders
.png){kind=icon}
OA
AI
DOI：10.1109/TCCN.2020.2990773.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A new approach for blind channel equalization and decoding, variational inference, and variational autoencoders (VAEs) in particular, is introduced. We first consider the reconstruction of uncoded data symbols transmitted over a noisy linear intersymbol interference (ISI) channel, with an unknown impulse response, without using pilot symbols. We derive an approximate maximum likelihood estimate to the channel parameters and reconstruct the transmitted data. We demonstrate significant and consistent improvements in the error rate of the reconstructed symbols, compared to existing blind equalization methods such as constant modulus, thus enabling faster channel acquisition. The VAE equalizer uses a convolutional neural network with a small number of free parameters. These results are extended to blind equalization over a noisy nonlinear ISI channel with unknown parameters. We then consider coded communication using low-density parity-check (LDPC) codes transmitted over a noisy linear or nonlinear ISI channel. The goal is to reconstruct the transmitted message from the channel observations corresponding to a transmitted codeword, without using pilot symbols. We demonstrate improvements compared to the expectation maximization (EM) algorithm using turbo equalization. Furthermore, unlike EM, the computational complexity of our method does not have exponential dependence on the size of the channel impulse response.
Keyword:
Blind equalizers
maximum likelihood estimation
deep learning
convolutional neural networks
belief propagation
期刊
I
IF:
7
论文数:
1.6K
被引数:
5.5K
机构
引用论文
Probing the Influence of Amino Acids on Photoluminescence from Carbon Nanotubes Suspended with DNA
.png){kind=icon}
PREAI
分享收藏
Interactions between Cells and Collagen V Molecules or Single Chains Involve Distinct Mechanisms
PREAI
分享收藏
查看更多内容