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Title: List Decoding of Polar Codes: How Large Should the List Be to Achieve ML Decoding?
Award ID(s):
1764104
PAR ID:
10406597
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
2021 IEEE International Symposium on Information Theory (ISIT)
Page Range / eLocation ID:
1594 to 1599
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Successive cancellation list decoding of polar codes provides very good performance for short to moderate block lengths. However, the list size required to approach the performance of maximum-likelihood decoding is still not well understood theoretically. This work identifies information-theoretic quantities that are closely related to this required list size. It also provides a natural approximation for these quantities that can be computed efficiently even for very long codes. Simulation results are provided for the binary erasure channel as well as the binary-input additive white Gaussian noise channel. 
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