Improved Automorphism Ensemble Decoder for Polar Codes
In this work, we propose an improved automorphism ensemble (AE) decoder for polar codes. With successive cancellation (SC) variant automorphisms, multiple decoding paths in the AE decoder produce their estimates of the transmitted codeword. In the proposed scheme, the decoding results from the multiple paths are cleverly utilized to generate a new channel output with which an additional decoding path is established. Performance evaluations clearly demonstrate that the proposed decoder achieves significantly improved error-rate performance as compared to the existing AE decoder for polar codes.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Near Maximum Likelihood Decoding with Deep Learning
A novel and efficient neural decoder algorithm is proposed. The proposed decoder is based on the neural Belief Propagation algorithm and the Automorphism Group. By combining neural belief propagation with permutations fr…
DecoderDeep LearningLeveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding
Syndrome-based neural decoding (SBND) has emerged as a promising deep learning approach for soft-decision decoding of high-rate, short-length codes. However, this approach still has substantial room for improvement. In t…
Data AugmentationCRISP: Curriculum based Sequential Neural Decoders for Polar Code Family
Polar codes are widely used state-of-the-art codes for reliable communication that have recently been included in the 5th generation wireless standards (5G). However, there remains room for the design of polar decoders t…
DecoderA Gated Hypernet Decoder for Polar Codes
Hypernetworks were recently shown to improve the performance of message passing algorithms for decoding error correcting codes. In this work, we demonstrate how hypernetworks can be applied to decode polar codes by emplo…
DecoderAction-List Reinforcement Learning Syndrome Decoding for Binary Linear Block Codes
This paper explores the application of reinforcement learning techniques to enhance the performance of decoding of linear block codes based on flipping bits and finding optimal decisions. We describe the methodology for …
Reinforcement Learning