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Generalization bounds and algorithms for learning to communicate over additive noise channels
N Weinberger - IEEE Transactions on Information Theory, 2021 - ieeexplore.ieee.org
An additive noise channel is considered, in which the distribution of the noise is
nonparametric and unknown. The problem of learning encoders and decoders based on …
nonparametric and unknown. The problem of learning encoders and decoders based on …
Learning maximum margin channel decoders
A Tsvieli, N Weinberger - IEEE Transactions on Information …, 2023 - ieeexplore.ieee.org
The problem of learning a channel decoder is considered for two channel models. The first
model is an additive noise channel whose noise distribution is unknown and nonparametric …
model is an additive noise channel whose noise distribution is unknown and nonparametric …
Learning maximum margin channel decoders for non-linear gaussian channels
A Tsvieli, N Weinberger - 2022 IEEE International Symposium …, 2022 - ieeexplore.ieee.org
The problem of learning a channel decoder for an unknown non-linear white Gaussian
noise channel is considered. The learner is provided with a fixed codebook and a dataset …
noise channel is considered. The learner is provided with a fixed codebook and a dataset …
ConvAE-Advanced: Adaptive transmission across multiple timeslots for error resilient operation
Recent advancements in machine learning for communications show that channel
autoencoders could revolutionize conventional communication systems through end-to-end …
autoencoders could revolutionize conventional communication systems through end-to-end …
Gradient feedback framework for joint transceiver neural network training
A method of wireless communication performed by a receiving device includes determining
a transmission reference point value and determining a transmission reference point …
a transmission reference point value and determining a transmission reference point …