Unsupervised Linear and Nonlinear Channel Equalization and Decoding Using Variational Autoencoders

Caciularu, Avi; Burshtein, David

doi:10.1109/tccn.2020.2990773

Cited by 45 publications

(22 citation statements)

References 43 publications

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“…One approach is to use a machine-learning algorithm as a replacement to one or more components of the system. Examples include [28] for channel equalization, [15] for channel encoder and decoder, [7,8,14] for channel decoder, and [29][30][31] for channel estimation. A second approach is to modify an existing algorithm by incorporating DNNs.…”

Section: Other Related Workmentioning

confidence: 99%

Learning Maximum Margin Channel Decoders

Tsvieli¹,

Weinberger²

2022

Preprint

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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. The learner is provided with a fixed codebook and a dataset comprised of independent samples of the noise, and is required to select a precision matrix for a nearest neighbor decoder in terms of the Mahalanobis distance. The second model is a non-linear channel with additive white Gaussian noise and unknown channel transformation. The learner is provided with a fixed codebook and a dataset comprised of independent input-output samples of the channel, and is required to select a matrix for a nearest neighbor decoder with a linear kernel. For both models, the objective of maximizing the margin of the decoder is addressed. Accordingly, for each channel model, a regularized loss minimization problem with a codebook-related regularization term and hinge-like loss function is developed, which is inspired by the support vector machine paradigm for classification problems. Expected generalization error bounds for the error probability loss function are provided for both models, under optimal choice of the regularization parameter. For the additive noise channel, a theoretical guidance for choosing the training signal-to-noise ratio is proposed based on this bound. In addition, for the non-linear channel, a high probability uniform generalization error bound is provided for the hypothesis class.For each channel, a stochastic sub-gradient descent algorithm for solving the regularized loss minimization problem is proposed, and an optimization error bound is stated. The performance of the proposed algorithms is demonstrated through several examples.

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Section: Other Related Workmentioning

confidence: 99%

Learning Maximum Margin Channel Decoders

Tsvieli¹,

Weinberger²

2022

Preprint

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“…A model-free approach based on reinforcement learning is proposed in [17]. Using advances in unsupervised learning, also blind channel equalization can be improved [18].…”

Section: A ML In Communicationsmentioning

confidence: 99%

CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection With Low Complexity

Beck

Bockelmann

Dekorsy

2021

IEEE Trans. Commun.

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Following the great success of Machine Learning (ML), especially Deep Neural Networks (DNNs), in many research domains in 2010s, several ML-based approaches were proposed for detection in large inverse linear problems, e.g., massive MIMO systems. The main motivation behind is that the complexity of Maximum A-Posteriori (MAP) detection grows exponentially with system dimensions. Instead of using DNNs, essentially being a black-box, we take a slightly different approach and introduce a probabilistic Continuous relaxation of disCrete variables to MAP detection. Enabling close approximation and continuous optimization, we derive an iterative detection algorithm: Concrete MAP Detection (CMD). Furthermore, extending CMD by the idea of deep unfolding into CMDNet, we allow for (online) optimization of a small number of parameters to different working points while limiting complexity. In contrast to recent DNN-based approaches, we select the optimization criterion and output of CMDNet based on information theory and are thus able to learn approximate probabilities of the individual optimal detector. This is crucial for soft decoding in today's communication systems. Numerical simulation results in MIMO systems reveal CMDNet to feature a promising accuracy complexity trade-off compared to State of the Art. Notably, we demonstrate CMDNet's soft outputs to be reliable for decoders.

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“…Furthermore, (Kim et al, 2018a;b) present a novel method for designing new error correcting codes by neural networks. In (Caciularu & Burshtein, 2020) a neural channel equalization and decoding using variational autoencoders is introduced. A deep soft interference cancellation for MIMO Detection are present in (Shlezinger et al, 2020).…”

Section: Error Correcting Codes With Deep Learningmentioning

confidence: 99%

Recovering AES Keys with a Deep Cold Boot Attack

Zimerman,

Nachmani,

Wolf

2021

Preprint

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Cold boot attacks inspect the corrupted random access memory soon after the power has been shut down. While most of the bits have been corrupted, many bits, at random locations, have not. Since the keys in many encryption schemes are being expanded in memory into longer keys with fixed redundancies, the keys can often be restored. In this work, we combine a novel cryptographic variant of a deep error correcting code technique with a modified SAT solver scheme to apply the attack on AES keys. Even though AES consists of Rijndael S-box elements, that are specifically designed to be resistant to linear and differential cryptanalysis, our method provides a novel formalization of the AES key scheduling as a computational graph, which is implemented by a neural message passing network. Our results show that our methods outperform the state of the art attack methods by a very large margin.

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Unsupervised Linear and Nonlinear Channel Equalization and Decoding Using Variational Autoencoders

Cited by 45 publications

References 43 publications

Learning Maximum Margin Channel Decoders

Learning Maximum Margin Channel Decoders

CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection With Low Complexity

Recovering AES Keys with a Deep Cold Boot Attack

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