Method of parametric adaptation of parallel and sequential turbo codes using neural networks
DOI:
https://doi.org/10.32347/2411-4049.2026.1.214-224Keywords:
5G, 6G, wireless technologies, turbo codes, neural networks, multi-layer perceptron, decoding uncertaintyAbstract
The work is devoted to the study of increasing the efficiency of functioning of modern wireless technologies 5G and 6G. The article presents a method of parametric adaptation of parallel and sequential turbo codes using neural networks of the multilevel perceptron type and the decoding uncertainty indicator.
The use of neural networks of the multilevel perceptron type for adjusting the external logarithmic ratios of the likelihood functions of probabilistic algorithms for decoding parallel and sequential turbo codes is considered.
Turbo codes are decoded using the maximum a posteriori probabilities (MAP) decoding algorithm, which calculates the posterior probability of each decoded symbol, minimizing the probability of an information symbol (bit) error.
The aim of the work is to develop a method for parametric adaptation of parallel and sequential turbo codes using neural networks of the multilevel perceptron type and a decoding uncertainty indicator.
The use of the decoding uncertainty indicator for parallel and sequential turbo codes at the training stage when determining the weight coefficients of the weight matrix and when functioning of neural networks is proposed.
References
Aldossari, S. A., Aldosary, A., & Chen, K.-C. (2023). Overcoming wireless channelmodelling and relay signal selection via artificial intelligence techniques in 5G and beyond.In Proceedings of the 14th International Conference on Ubiquitous and Future Networks(ICUFN) (pp. 810–815). https://doi.org/10.1109/ICUFN57995.2023.10200723
Thamilchelvan, R., & Gomathy, C. (2023). Modeling intelligent wireless communicationchannel for 5G and beyond using advanced machine learning techniques. In Proceedings ofthe 5th International Conference on Inventive Research in Computing Applications (ICIRCA)(pp. 916–920). https://doi.org/10.1109/ICIRCA57980.2023.10220931
Al-Khafaji, M., & Elwiya, L. (2022). ML/AI empowered 5G and beyond networks. InProceedings of the International Congress on Human-Computer Interaction, Optimization andRobotic Applications (HORA) (pp. 1–6). https://doi.org/10.1109/HORA55278.2022.9799813
Olaniyi, K. A., Heymann, R., & Swart, T. G. (2024). Machine learning for channelcoding: A paradigm shift from FEC codes. Journal of Communications, 19(2), 107–118.
Richardson, T. J., Shokrollahi, M. A., & Urbanke, R. L. (2001). Design of capacity-approaching irregular low-density parity-check codes. IEEE Transactions on InformationTheory, 47(2), 619–637. https://doi.org/10.1109/18.910578
Benedetto, S., & Montorsi, G. (1996). Principles of turbo coding. Springer.
Arıkan, E. (2009). Channel polarization: A method for constructing capacity-achievingcodes for symmetric binary-input memoryless channels. IEEE Transactions on InformationTheory, 55(7), 3051–3073. https://doi.org/10.1109/TIT.2009.2021379
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