Blind Channel Codes Recognition via Deep Learning

Shen, Boxiao; Huang, Chuan; Xu, Wenjun; Yang, Tingting; Cui, Shuguang

doi:10.1109/jsac.2021.3087252

Cited by 18 publications

(7 citation statements)

References 47 publications

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“…Similar to [17]- [30], we consider that the code parameters inside the candidate set are known, i.e., the set of corresponding generating matrices is known. It is noteworthy to mention that the problem addressed here is different from recognition of the coding scheme itself (e.g., recognition between LDPC and convolutional) as in [13]- [16].…”

Section: B Problem Definition: Blind Channel Code Recognitionmentioning

confidence: 99%

“…The blind recognition algorithms in the second category, however, recover the parameter(s) only among a candidate set of parameters. This is because many standard AMC schemes do not freely adjust all possible parameters, and instead, only select among a set of pre-defined parameters [13]- [30]. Our solution falls in this category, i.e., recovers the code parameters among a candidate set.…”

Section: Introduction Forward Error Correcting (Fec) Codes Have Been ...mentioning

confidence: 99%

“…In [13]- [16], the candidate set consists of different coding schemes. For instance, the blind recognition algorithm proposed in [16] recovers the selected coding scheme among QC-LDPC, SC-LDPC, and convolutional codes.…”

Section: Introduction Forward Error Correcting (Fec) Codes Have Been ...mentioning

confidence: 99%

“…In most real applications, however, channel impairments such as fading and multi-paths effects need to be considered in devising a resilient solution. Recently, deep-learning based methods have been employed in blind recognition of coding parameters [13], [15], [16], [24]. Deep-learning based methods are more resilient to signal conditions compared to classical approaches.…”

Section: Introduction Forward Error Correcting (Fec) Codes Have Been ...mentioning

confidence: 99%

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Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions

Dehdashtian,

Hashemi,

Salehkaleybar

2020

Preprint

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We consider the problem of recovering channel code parameters over a candidate set by merely analyzing the received encoded signals. We propose a deep learning-based solution that I) is capable of identifying the channel code parameters for any coding scheme (such as LDPC, Convolutional, Turbo, and Polar codes), II) is robust against channel impairments like multipath fading, III) does not require any previous knowledge or estimation of channel state or signal-to-noise ratio (SNR), and IV) outperforms related works in terms of probability of detecting the correct code parameters.

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Section: B Problem Definition: Blind Channel Code Recognitionmentioning

confidence: 99%

Section: Introduction Forward Error Correcting (Fec) Codes Have Been ...mentioning

confidence: 99%

Section: Introduction Forward Error Correcting (Fec) Codes Have Been ...mentioning

confidence: 99%

Section: Introduction Forward Error Correcting (Fec) Codes Have Been ...mentioning

confidence: 99%

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Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions

Dehdashtian,

Hashemi,

Salehkaleybar

2020

Preprint

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“…Due to its powerful capability in coping with nonlinear problems [13], machine learning (ML) is a promising solution to tackle the challenge of nonlinear effects in the TS stage and also aroused extensive interest in the design of the physical layer communications [14][15][16][17][18][19] over the past few years. In ML-based applications, substantial researches have proven that the predictive performance of supervised learning outperforms that of unsupervised learning because the supervised ones could directly take feedback for the prediction [20].…”

Section: Introductionmentioning

confidence: 99%

ELM‐based timing synchronization for OFDM systems by exploiting computer‐aided training strategy

et al. 2023

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Due to the implementation bottleneck of training data collection in realistic wireless communications systems, supervised learning‐based timing synchronization (TS) is challenged by the incompleteness of training data. To tackle this bottleneck, the computer‐aided approach is extended, with which the local device can generate the training data instead of generating learning labels from the received samples collected in realistic systems, and then construct an extreme learning machine (ELM)‐based TS network in orthogonal frequency division multiplexing (OFDM) systems. Specifically, by leveraging the rough information of channel impulse responses (CIRs), i.e. root‐mean‐square (r.m.s) delay, the loose constraint‐based and flexible constraint‐based training strategies are proposed for the learning‐label design against the maximum multi‐path delay. The underlying mechanism is to improve the completeness of multi‐path delays that may appear in the realistic wireless channels and thus increase the statistical efficiency of the designed TS learner. By this means, the proposed ELM‐based TS network can alleviate the degradation of generalization performance. Numerical results reveal the robustness and generalization of the proposed scheme against varying parameters.

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Blind recognition of channel codes based on a multiscale dilated convolution neural network

Liu,

Cao,

2024

Physical Communication

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Blind Channel Codes Recognition via Deep Learning

Cited by 18 publications

References 47 publications

Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions

Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions

ELM‐based timing synchronization for OFDM systems by exploiting computer‐aided training strategy

Blind recognition of channel codes based on a multiscale dilated convolution neural network

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