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Learning to Construct Nested Polar Codes: An Attention-Based Set-to-Element Model
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AI
DOI：10.1109/LCOMM.2021.3114118.png)
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摘要
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As capacity-achieving codes under successive cancellation (SC) decoding, nested polar codes have been adopted in 5G enhanced mobile broadband. To optimize the performance of the code construction under practical decoding, e.g. SC list (SCL) decoding, artificial intelligence based methods have been explored in the literature. However, the structure of nested polar codes has not been fully exploited for code construction. To address this issue, this letter transforms the original combinatorial optimization problem for the construction of nested polar codes into a policy optimization problem for sequential decision, and proposes an attention-based set-to-element model, which incorporates the nested structure into the policy design. Based on the proposed architecture for the policy, a gradient based algorithm for code construction and a divide-and-conquer strategy for parallel implementation are further developed. Simulation results demonstrate that the proposed construction outperforms the state-of-the-art nested polar codes for SCL decoding.
Keyword:
Polar codes
Codes
Decoding
Neural networks
Optimization
Reliability
Transforms
Nested polar codes
learning to optimize
neural networks
attention mechanism
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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