Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability
- URL: http://arxiv.org/abs/2301.12231v2
- Date: Tue, 31 Jan 2023 09:29:19 GMT
- Title: Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability
- Authors: Vukan Ninkovic, Dejan Vukobratovic, Christian H\"ager, Henk Wymeersch,
Alexandre Graell i Amat
- Abstract summary: This paper proposes a novel rateless autoencoder (AE)-based code design suitable for decoding the transmitted message before the noisy codeword is fully received.
The proposed rateless AEs significantly outperform the conventional AE designs for scenarios where it is desirable to trade off reliability for lower decoding delay.
- Score: 90.17852645780945
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Most of today's communication systems are designed to target reliable message
recovery after receiving the entire encoded message (codeword). However, in
many practical scenarios, the transmission process may be interrupted before
receiving the complete codeword. This paper proposes a novel rateless
autoencoder (AE)-based code design suitable for decoding the transmitted
message before the noisy codeword is fully received. Using particular dropout
strategies applied during the training process, rateless AE codes allow to
trade off between decoding delay and reliability, providing a graceful
improvement of the latter with each additionally received codeword symbol. The
proposed rateless AEs significantly outperform the conventional AE designs for
scenarios where it is desirable to trade off reliability for lower decoding
delay.
Related papers
- Threshold Selection for Iterative Decoding of $(v,w)$-regular Binary Codes [84.0257274213152]
Iterative bit flipping decoders are an efficient choice for sparse $(v,w)$-regular codes.
We propose concrete criteria for threshold determination, backed by a closed form model.
arXiv Detail & Related papers (2025-01-23T17:38:22Z) - Enhanced Min-Sum Decoding of Quantum Codes Using Previous Iteration Dynamics [3.6048794343841766]
We propose a novel message-passing decoding approach that leverages the degeneracy of quantum low-density parity-check codes.
Our focus is on two-block Calderbank-Shor-Steane (CSS) codes, which are composed of symmetric stabilizers.
arXiv Detail & Related papers (2025-01-09T07:28:26Z) - Rateless Stochastic Coding for Delay-Constrained Semantic Communication [5.882972817816777]
We consider the problem of joint source-channel coding for semantic communication from a rateless perspective.
We propose a more general communication objective that minimizes the perceptual distance by incorporating a semantic-level reconstruction objective.
We show that the proposed rateless distortion coding scheme can achieve variable rates of transmission maintaining an excellent trade-off between distortion and perception.
arXiv Detail & Related papers (2024-06-28T10:27:06Z) - Learning Linear Block Error Correction Codes [62.25533750469467]
We propose for the first time a unified encoder-decoder training of binary linear block codes.
We also propose a novel Transformer model in which the self-attention masking is performed in a differentiable fashion for the efficient backpropagation of the code gradient.
arXiv Detail & Related papers (2024-05-07T06:47:12Z) - An Effective Mixture-Of-Experts Approach For Code-Switching Speech
Recognition Leveraging Encoder Disentanglement [9.28943772676672]
Codeswitching phenomenon remains a major obstacle that hinders automatic speech recognition.
We introduce a novel disentanglement loss to enable the lower-layer of the encoder to capture inter-lingual acoustic information.
We verify that our proposed method outperforms the prior-art methods using pretrained dual-encoders.
arXiv Detail & Related papers (2024-02-27T04:08:59Z) - Drop your Decoder: Pre-training with Bag-of-Word Prediction for Dense Passage Retrieval [26.00149743478937]
Masked auto-encoder pre-training has emerged as a prevalent technique for initializing and enhancing dense retrieval systems.
We propose a modification to the traditional MAE by replacing the decoder of a masked auto-encoder with a completely simplified Bag-of-Word prediction task.
Our proposed method achieves state-of-the-art retrieval performance on several large-scale retrieval benchmarks without requiring any additional parameters.
arXiv Detail & Related papers (2024-01-20T15:02:33Z) - Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial
Attacks [70.51799606279883]
We introduce test-time adversarial attacks on deep neural networks (DNNs) for semantic communications.
We show that it is possible to change the semantics of the transferred information even when the reconstruction loss remains low.
arXiv Detail & Related papers (2022-12-20T17:13:22Z) - Denoising Diffusion Error Correction Codes [92.10654749898927]
Recently, neural decoders have demonstrated their advantage over classical decoding techniques.
Recent state-of-the-art neural decoders suffer from high complexity and lack the important iterative scheme characteristic of many legacy decoders.
We propose to employ denoising diffusion models for the soft decoding of linear codes at arbitrary block lengths.
arXiv Detail & Related papers (2022-09-16T11:00:50Z) - Adversarial Neural Networks for Error Correcting Codes [76.70040964453638]
We introduce a general framework to boost the performance and applicability of machine learning (ML) models.
We propose to combine ML decoders with a competing discriminator network that tries to distinguish between codewords and noisy words.
Our framework is game-theoretic, motivated by generative adversarial networks (GANs)
arXiv Detail & Related papers (2021-12-21T19:14:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.