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20182026
most citedLearning to Flip Successive Cancellation Decoding of Polar Codes with LSTM Networks

6 citations · 19 across the 13 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

eess.SP20191 cited

Realistic Channel Models Pre-training

Yourui Huangfu, Jian Wang, Chen Xu +5

In this paper, we propose a neural-network-based realistic channel model with both the similar accuracy as deterministic channel models and uniformity as stochastic channel models.…

cs.IT20192 cited

An Asymmetric Adaptive SCL Decoder Hardware for Ultra-Low-Error-Rate Polar Codes

Jiajie Tong, Huazi Zhang, Lingchen Huang +2

In theory, Polar codes do not exhibit an error floor under successive-cancellation (SC) decoding. In practice, frame error rate (FER) down to has not been reported with…

cs.IT2019

Reinforcement Learning for Nested Polar Code Construction

Lingchen Huang, Huazi Zhang, Rong Li +2

In this paper, we model nested polar code construction as a Markov decision process (MDP), and tackle it with advanced reinforcement learning (RL) techniques. First, an MDP environ…

cs.IT20196 cited

Learning to Flip Successive Cancellation Decoding of Polar Codes with LSTM Networks

Xianbin Wang, Huazi Zhang, Rong Li +4

The key to successive cancellation (SC) flip decoding of polar codes is to accurately identify the first error bit. The optimal flipping strategy is considered difficult due to lac…

cs.IT2019

AI Coding: Learning to Construct Error Correction Codes

Lingchen Huang, Huazi Zhang, Rong Li +2

In this paper, we investigate an artificial-intelligence (AI) driven approach to design error correction codes (ECC). Classic error correction code was designed upon coding theory…