In this work, a novel data-driven methodology for designing polar codes is proposed. The methodology is suitable for the case where the channel is given as a âblack-boxâ and the designer has access to the channel for generating observations of its inputs and outputs, but does not have access to the explicit channel model. The methodology consists of two components: (1) a neural estimation of the sufficient statistic of the channel outputs using recent advances in Kullback Leibler (KL) estimation, and (2) a neural successive cancellation (NSC) decoder using three neural networks that replace the core elements of the successive cancellation (SC) decoder. The parameters of the neural networks are determined during a training phase where the mutual information of the effective channels is estimated. We demonstrate the performance of the algorithm on memoryless channels and on finite state channels. Then, we compare the results with the optimal decoding given by the SC and SC trellis decoders, respectively.
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CRISP: 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 still remains room for design of polar decoders that are both efficient and reliable in the short blocklength regime. Motivated by recent successes of data-driven channel decoders, we introduce a novel đurđđculum based đequential neural decoder for đolar codes (CRISP). We design a principled curriculum, guided by information-theoretic insights, to train CRISP and show that it outperforms the successive-cancellation (SC) decoder and attains near-optimal reliability performance on the Polar(32,16) and Polar(64,22) codes. The choice of the proposed curriculum is critical in achieving the accuracy gains of CRISP, as we show by comparing against other curricula. More notably, CRISP can be readily extended to Polarization-Adjusted-Convolutional (PAC) codes, where existing SC decoders are significantly less reliable. To the best of our knowledge, CRISP constructs the first data-driven decoder for PAC codes and attains near-optimal performance on the PAC(32,16) code.
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- Award ID(s):
- 2002664
- NSF-PAR ID:
- 10481827
- Publisher / Repository:
- Proceedings of Machine Learning Research
- Date Published:
- Journal Name:
- Proceedings of the 40th International Conference on Machine Learning
- Volume:
- 202
- Page Range / eLocation ID:
- 12823-12845
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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