Machine learning for decoding linear block codes: case of multi-class logistic regression model

Imrane, Chemseddine Idrissi; Nouh, Saïd; Mehdi, Bellfkih El; Seddiq, El Kasmi Alaoui; Marzak, Abdelaziz

doi:10.11591/ijeecs.v24.i1.pp538-547

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…The Table 4 gives a comparison of scores between EL-BoostDec and logistic regression decoder (LRDec) [8] accuracy in the training phase. It indicates the score for some codes in the training process.…”

Section: Comparison Between El-boostdec and Some Competitors 361 Comp...mentioning

confidence: 99%

“…Other approaches make use of local search and genetic algorithms. Some articles [7], [8] present some learning-based algorithms for error correction. A new deep-learning technique for enhancing the belief propagation (BP) algorithm for decoding linear block codes is presented in [7].…”

Section: Introductionmentioning

confidence: 99%

“…A new deep-learning technique for enhancing the belief propagation (BP) algorithm for decoding linear block codes is presented in [7]. Imrane et al [8] used a machine learning approach along with a syndrome calculation to enhance the performance of another BP-based technique, which is applicable to BCH and QR codes, in terms of bit error rate (BER) and time complexity. In the same spirit, Nachmani et al [9] have presented an architecture for recurrent neural networks that can correct errors efficiently.…”

Section: Introductionmentioning

confidence: 99%

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A new efficient decoder of linear block codes based on ensemble learning models: case of boosting

El Assad,

Nouh,

Chemseddine Idrissi

et al. 2024

IJ-AI

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<p>Error-correcting codes are used to partially or completely correct errors as much as possible, while ensuring high transmission speeds. Several Machine Learning (ML) models such as Logistic Regression and Decision tree have been applied to correct transmission errors. Among the most powerful ML techniques are aggregation methods which have yielded to excellent results in many areas of research. It is this excellence that has prompted us to consider their application for the hard decoding problem. In this sense, we have successfully designed, tested and validated our proposed EL-BoostDec decoder (hard decision decoder based on Ensemble Learning - Boosting technique) which is based on computing of the syndrome of the received word and on using Ensemble Learning techniques to find the corresponding corrigible error. The obtained results with EL-BoostDec are very encouraging in terms of the binary error rate (BER) that it offers. Practically EL-BoostDec has succeed to correct 100% of errors that have weights less than or equal to the correction capacity of studied codes. The comparison of EL-BoostDec with many competitors proves its power. A study of parameters which impact on EL-BoostDec performances has been established to obtain a good BER with minimum run time complexity.</p>

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Section: Comparison Between El-boostdec and Some Competitors 361 Comp...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new efficient decoder of linear block codes based on ensemble learning models: case of boosting

El Assad,

Nouh,

Chemseddine Idrissi

et al. 2024

IJ-AI

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“…In [19], Imrane et al proposed a multi-class logistic regression model for decoding linear block codes , showcasing the potential of machine learning in error correction, this paper has enhanced in terms of performances using 2 models decoding [20]. Nachmani et al investigated learning to decode linear codes using deep learning in [21], presenting promising results in utilizing deep learning methods for error correction.…”

Section: A the Overview Of Machine Learning Decodersmentioning

confidence: 99%

Error-Correcting Codes and the Power of Machine Learning: Enhancing Data Reliability

Idrissi,

Nouh,

Bellfkih

et al. 2023

2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA)

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Error-correcting codes and machine learning algorithms are two fundamental pillars in ensuring the reliability and accuracy of data transmission and storage systems. Errorcorrecting codes have long been instrumental in detecting and correcting errors during data communication and retrieval, while machine learning techniques have emerged as powerful tools for data analysis and pattern recognition. Integrating machine learning into error correction processes presents exciting opportunities to enhance error detection and correction performance, especially in complex and dynamic environments. In this article, we provide an in-depth overview of error-correcting codes, including traditional techniques like Hamming codes, Reed-Solomon codes, Turbo codes, and Convolutional codes. We explore the challenges faced by traditional error correction methods in handling modern data complexities. Subsequently, we delve into the integration of machine learning algorithms, such as neural networks, support vector machines, and decision trees, for error correction tasks. We discuss how machine learning enables adaptive, efficient, and dynamic error correction strategies. Furthermore, we examine real-world applications of machine learning-based error correction in communication systems, data storage, DNA sequencing, and quantum computing. Additionally, we highlight the challenges that researchers and engineers need to overcome, such as computational complexity, real-time processing, and adversarial attacks. Ultimately, the collaboration between error-correcting codes and machine learning offers exciting prospects for a future where data transmission and storage systems are more secure, efficient, and adaptable. By addressing challenges and embracing innovations, we can pave the way for error-free data management in an ever-evolving data-driven world.

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Optimizing Spatial Complexity of the Hard Decision Decoder Based on Hash and Syndrome Decoding (HSDec)

El Kasmi Alaoui,

Chanyour,

Faham

et al. 2024

Scientific African

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Machine learning for decoding linear block codes: case of multi-class logistic regression model

Cited by 4 publications

References 22 publications

A new efficient decoder of linear block codes based on ensemble learning models: case of boosting

A new efficient decoder of linear block codes based on ensemble learning models: case of boosting

Error-Correcting Codes and the Power of Machine Learning: Enhancing Data Reliability

Optimizing Spatial Complexity of the Hard Decision Decoder Based on Hash and Syndrome Decoding (HSDec)

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