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Found 10 papers, 2 papers with code
Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
We propose a novel frequency-domain blind equalization scheme for coherent optical communications.
Blind Channel Equalization Using Vector-Quantized Variational Autoencoders
Furthermore, we show that for the linear channel, the proposed scheme exhibits better convergence properties than the \ac{MMSE}-based, the \ac{CMA}-based, and the \ac{VAE}-based equalizers in terms of both convergence speed and robustness to variations in training batch size and learning rate.
Spatial Signal Design for Positioning via End-to-End Learning
This letter considers the problem of end-to-end learning for joint optimization of transmitter precoding and receiver processing for mmWave downlink positioning.
Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware Impairments
Simulation results show that the reinforcement-learning-based algorithm achieves similar performance to the standard supervised end-to-end learning approach assuming perfect channel knowledge.
Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning
We propose an over-the-air digital predistortion optimization algorithm using reinforcement learning.
End-to-End Learning for Integrated Sensing and Communication
The approach includes the proposal of the AE architecture, a novel ISAC loss function, and the training procedure.
Over-the-fiber Digital Predistortion Using Reinforcement Learning
We demonstrate, for the first time, experimental over-the-fiber training of transmitter neural networks (NNs) using reinforcement learning.
End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairment
We propose an end-to-end learning-based approach for superchannel systems impaired by non-ideal hardware component.
Benchmarking End-to-end Learning of MIMO Physical-Layer Communication
End-to-end data-driven machine learning (ML) of multiple-input multiple-output (MIMO) systems has been shown to have the potential of exceeding the performance of engineered MIMO transceivers, without any a priori knowledge of communication-theoretic principles.
Learning Physical-Layer Communication with Quantized Feedback
A novel quantization method is proposed, which exploits the specific properties of the feedback signal and is suitable for non-stationary signal distributions.
