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Enhance belief propagation decoding using ensemble learning approaches

Md Aleem, Sharjeel Ahmad, Syed Jalal Ahmad

International Journal of Networking and Virtual OrganisationsJan 1, 20250 citations

Hierarchical Subcode Ensemble Belief Propagation Decoding of Polar Codes

Yubeen Jo, Geon Choi, Chanho Park +1 more

IEEE Communications LettersJan 1, 20260 citations

Decoding of Polar Codes Based on Q-Learning-Driven Belief Propagation

Laisa Marianna Soares Oliveira, Robert M. Oliveira, Rodrigo C. de Lamare

This paper presents an enhanced belief propagation (BP) decoding algorithm and a reinforcement learning-based BP decoding algorithm for polar codes. The enhanced BP algorithm weighs each Processing Element (PE) input based on their signals and Euclidean distances using a heuristic metric. The proposed reinforcement learning-based BP decoding strategy relies on reweighting the messages and consists of two steps: we first weight each PE input based on their signals and Euclidean distances using a heuristic metric, then a Q-learning algorithm (QLBP) is employed to figure out the best correction factor for successful decoding. Simulations show that the proposed enhanced BP and QLBP decoders outperform the successive cancellation (SC) and belief propagation (BP) decoders, and approach the SCL decoders.This paper presents an enhanced belief propagation (BP) decoding algorithm and a reinforcement learning-based BP decoding algorithm for polar codes. The enhanced BP algorithm weighs each Processing Element (PE) input based on their signals and Euclidean distances using a heuristic metric. The proposed reinforcement learning-based BP decoding strategy relies on reweighting the messages and consists of two steps: we first weight each PE input based on their signals and Euclidean distances using a heuristic metric, then a Q-learning algorithm (QLBP) is employed to figure out the best correction factor for successful decoding. Simulations show that the proposed enhanced BP and QLBP decoders outperform the successive cancellation (SC) and belief propagation (BP) decoders, and approach the SCL decoders.

arXiv:2111.00673Nov 1, 20211 citation

Parallel versus sequential updating for belief propagation decoding

Haggai Kfir, Ido Kanter

arXiv:cond-mat/0207185Physica A Statistical Mechanics and its ApplicationsSep 22, 200397 citations

Multi-scale error-correcting codes and their decoding using belief propagation

Yongseok Yoo

May 1, 20141 citation

Q-Learning-Driven BP decoding for Polar Codes

Lucas Maia de Oliveira, Robert M. Oliveira, Rodrigo C. de Lamare

This paper presents an enhanced belief propagation (BP) decoding algorithm and a reinforcement learning-based BP decoding algorithm for polar codes.The enhanced BP algorithm weighs each Processing Element (PE) input based on their signals and Euclidean distances using a heuristic metric.The proposed reinforcement learning-based BP decoding strategy relies on reweighting the messages and consists of two steps: we first weight each PE input based on their signals and Euclidean distances using a heuristic metric, then a Q-learning algorithm (QLBP) is employed to figure out the best correction factor for successful decoding.Simulations show that the proposed enhanced BP and QLBP decoders outperform the successive cancellation (SC) and belief propagation (BP) decoders, and approach the SCL decoders.This paper presents an enhanced belief propagation (BP) decoding algorithm and a reinforcement learning-based BP decoding algorithm for polar codes.The enhanced BP algorithm weighs each Processing Element (PE) input based on their signals and Euclidean distances using a heuristic metric.The proposed reinforcement learning-based BP decoding strategy relies on reweighting the messages and consists of two steps: we first weight each PE input based on their signals and Euclidean distances using a heuristic metric, then a Q-learning algorithm (QLBP) is employed to figure out the best correction factor for successful decoding.Simulations show that the proposed enhanced BP and QLBP decoders outperform the successive cancellation (SC) and belief propagation (BP) decoders, and approach the SCL decoders.

Anais de XXXV Simpósio Brasileiro de Telecomunicações e Processamento de SinaisJan 1, 20212 citations

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