List Decoding of Polar Codes: How Large Should the List Be to Achieve ML Decoding?
- Award ID(s):
- 1764104
- PAR ID:
- 10406597
- 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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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.more » « less
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