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Semi-Supervised Channel Equalization Using Variational Autoencoders
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DOI：10.1109/TWC.2024.3485991.png)
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Abstract
En 中文
We present methods for semi-supervised learning (SSL) from few pilots over nonlinear channels using variational autoencoders. These channels, which are unknown at the receiver, may have finite memory (intersymbol interference). The loss function we use for SSL incorporates both the labeled (pilot) symbols and unlabeled (payload) symbols. We demonstrate a very significant reduction in the number of pilot symbols required for reliable inference over the channel when applying SSL to train a variational autoencoder, compared to standard supervised learning of a neural network decoder using only pilot data information.
Keywords:
Symbols
Payloads
Receivers
Training
Memoryless systems
Channel estimation
Standards
Wireless communication
Vectors
Reliability
semi-supervised learning
variational inference
variational autoencoders
Journal
IF:
10.7
Papers:
1.3W
Citations:
5.3W
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