An Improved Min-Sum Polar Code Decoding Algorithm
Mingyuan Fang
Abstract
Mingyuan Fang
Abstract
In 2009, the presentation of polar code, the first theory to approach the Shannon limit of communication channel coding schemes. Thus, academics and business have focused on Polar codes. Research and worldwide standards have been offered one after another, establishing the groundwork for its real-world use.The paper proposes a min-sum decoding technique for enhanced Polar codes and revises the node update formula in the min-sum decoding algorithm by using the piecewise linear function to approximate the function ln cosh(x) in the belief propagation decoding algorithm. Compared to the min-sum decoding algorithm, the enhanced approach improves decoding performance while somewhat increasing complexity. Compared to the belief propagation decoding algorithm, this approach decreases computational complexity significantly and is simpler to implement in hardware. Based on the min-sum method and the belief propagation algorithm, this approach is offered as a balance between complexity and performance. The simulation results demonstrate that the enhanced min-sum decoding algorithm performs similarly to its predecessor. The performance of the degree propagation decoding method is almost identical to that of the min-sum decoding technique, which is superior.
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In 2009, the presentation of polar code, the first theory to approach the Shannon limit of communication channel coding schemes. Thus, academics and business have focused on Polar codes. Research and worldwide standards have been offered one after another, establishing the groundwork for its real-world use.The paper proposes a min-sum decoding technique for enhanced Polar codes and revises the node update formula in the min-sum decoding algorithm by using the piecewise linear function to approximate the function ln cosh(x) in the belief propagation decoding algorithm. Compared to the min-sum decoding algorithm, the enhanced approach improves decoding performance while somewhat increasing complexity. Compared to the belief propagation decoding algorithm, this approach decreases computational complexity significantly and is simpler to implement in hardware. Based on the min-sum method and the belief propagation algorithm, this approach is offered as a balance between complexity and performance. The simulation results demonstrate that the enhanced min-sum decoding algorithm performs similarly to its predecessor. The performance of the degree propagation decoding method is almost identical to that of the min-sum decoding technique, which is superior.
Key concepts: Decoding methods, Sequential decoding, Berlekamp–Welch algorithm, Belief propagation, List decoding, Algorithm, Computer science, Function (biology)