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

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


Facing the challenge of enormous data sets variety, several machine learning-based algorithms for prediction (e.g, Support Vector Machine, Multi Layer Perceptron and Logistic Regression.) have been highly proposed and used over the last years in many fields. Error correcting codes (ECCs) are extensively used in practice to protect data against damaged data storage systems and against random errors due to noise effects. In this paper, we will use machine learning methods, especially multi-class logistic regression combined with the famous syndrome decoding algorithm. The main idea behind our decoding method which we call Logistic Regression Decoder (LRDec) is to use the efficient multi-class logistic regression models to find errors from syndromes in linear codes such as Bose, Ray-Chaudhuri and Hocquenghem (BCH), and the Quadratic Residue (QR). Obtained results of the proposed decoder have a significant benefit in terms of Bit Error Rate (BER) for random binary codes. The comparison of our decoder with many competitors proves its power. The proposed decoder has reached a success percentage of 100% for correctable errors in the studied codes.


BCH codes; Error correction codes; Logistic regression; Machine learning; SoftMax; Syndrome decoding;



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