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Low Complexity List Decoding for Polar Codes with Multiple CRC Codes

College of Information and Communication Engineering, Sungkyunkwan University, Gyeonggi-do, Suwon 16419, Korea
Department of Computer Science, Ulsan College, Hwajeong-dong, Dong-gu, Ulsan 44022, Korea
Department of Information and Communication Engineering, Chosun University, Gwangju 61452, Korea
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Entropy 2017, 19(4), 183;
Received: 7 February 2017 / Revised: 22 March 2017 / Accepted: 11 April 2017 / Published: 24 April 2017
(This article belongs to the Section Information Theory, Probability and Statistics)
Polar codes are the first family of error correcting codes that provably achieve the capacity of symmetric binary-input discrete memoryless channels with low complexity. Since the development of polar codes, there have been many studies to improve their finite-length performance. As a result, polar codes are now adopted as a channel code for the control channel of 5G new radio of the 3rd generation partnership project. However, the decoder implementation is one of the big practical problems and low complexity decoding has been studied. This paper addresses a low complexity successive cancellation list decoding for polar codes utilizing multiple cyclic redundancy check (CRC) codes. While some research uses multiple CRC codes to reduce memory and time complexity, we consider the operational complexity of decoding, and reduce it by optimizing CRC positions in combination with a modified decoding operation. Resultingly, the proposed scheme obtains not only complexity reduction from early stopping of decoding, but also additional reduction from the reduced number of decoding paths. View Full-Text
Keywords: polar codes; successive cancellation list decoding; multiple CRC codes polar codes; successive cancellation list decoding; multiple CRC codes
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Kim, J.-H.; Kim, S.-H.; Jang, J.-W.; Kim, Y.-S. Low Complexity List Decoding for Polar Codes with Multiple CRC Codes. Entropy 2017, 19, 183.

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