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- W3130968093 abstract "In this paper we address the problem of selecting factor-graph permutations of polar codes under belief propagation (BP) decoding to significantly improve the error-correction performance of the code. In particular, we formalize the factor-graph permutation selection as the multi-armed bandit problem in reinforcement learning and propose a decoder that acts like an online-learning agent that learns to select the good factor-graph permutations during the course of decoding. We use state-of-the-art algorithms for the multi-armed bandit problem and show that for a 5G polar codes of length 128 with 64 information bits, the proposed decoder has an error-correction performance gain of around 0.125 dB at the target frame error rate of 10 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>-4</sup> , when compared to the approach that randomly selects the factor-graph permutations." @default.
- W3130968093 created "2021-03-01" @default.
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- W3130968093 date "2020-12-01" @default.
- W3130968093 modified "2023-10-10" @default.
- W3130968093 title "Decoding Polar Codes with Reinforcement Learning" @default.
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- W3130968093 doi "https://doi.org/10.1109/globecom42002.2020.9348007" @default.
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