Deep Learning Models for Decoding of LDPC Codes


Error correction coding (ECC) has an essential role in ensuring integrity of data in numerous applications, including data storage, transmission, and networking. Over the last few years, new interactions emerged between coding theory and machine learning, seen as a promising way to overcome the limitations of existing ECC solutions at short to medium code-lengths. For many known constructions of ECC codes, it turns out that these limitations are primarily due to the decoding algorithm, rather than the intrinsic error correction capability of the code. However, determining the appropriate machine learning models that apply to ECC decoding specificities is challenging, and current research still faces a significant gap to bridge to fundamental limits in the finite-length regime. This PhD project aims at expanding the current knowledge on machine learning based decoding of low-density parity-check (LDPC) codes, in several directions. First, it will investigate ensemble learning methods, in which multiple models are trained to solve the decoding problem and combined to get better results. Specific methods will be devised to ensure diversity of the individual models and to cover all the variability of the code structure. Second, it will explore knowledge distillation to transfer the superior performance of an ensemble to a single model, or from a large model to a smaller one, which is known to boost the prediction performance in several cases. Finally, the project will investigate syndrome-based decoding strategies, as a way to enable the use of powerful deep neural network models, rather than current belief-propagation based models, thus unleashing the full power of the above ensemble learning and knowledge distillation methods. The doctoral student will be hosted at CEA-Leti in Grenoble within a research team expert in signal processing for télécommunications (

Master IA ou Théorie de lapos;Information/Codage


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