Abstract
Polar codes using successive-cancellation decoding always suffer from high latency for its serial nature. Fast simplified successive-cancellation decoding algorithm improves the situation in theoretically but not performs well as expected in practical for the workload of nodes identification and the existence of many short blocks. Meanwhile, Neural network (NN) based decoders have appeared as potential candidates to replace conventional decoders for polar codes. But the exponentially increasing training complexity with information bits is unacceptable which means it is only suitable for short codes. In this paper, we present an improvement that increases decoding efficiency without degrading the error-correction performance. The long polar codes are divided into several sub-blocks, some of which can be decoded adopting fast maximum likelihood decoding method and the remained parts are replaced by several short codes NN decoders. The result shows that time steps the proposed algorithm need only equal to 79.8% of fast simplified successive-cancellation decoders require. Moreover, it has up to 21.2 times faster than successive-cancellation decoding algorithm. More importantly, the proposed algorithm decreases the hardness when applying in some degree.
Highlights
The long polar codes are divided into several sub-blocks, some of which can be decoded adopting fast maximum likelihood decoding method and the remained parts are replaced by several short codes Neural network (NN) decoders
Fast-SSC and NN-fSSC decoding has a negligible effect on error-correction performance which is affected slightly by code rate
In order to study the extent of improvement which can be achieved in decoding delay, two significant factors affecting the decoding speed of fast-SSC and NN-fSSC are observed
Summary
New algorithms are proposed to improve the error-correction performance of SC decoding algorithm such as successive cancellation list (SCL) decoding algorithm [2] [3] [4] [5] [6], successive cancellation stack (SCS) decoding algorithm. These algorithms realize better performance at the cost of higher computational complexity and lower throughput. To reduce the decoding latency without having a bad influence on the error-correction performance, simplified successive-cancellation (SSC) decoding was proposed to take advantage of rate-zero and rate-one nodes. NN decoding may be helpful to improve fast-SSC decoder, because NN decoder estimates the bits by passing each layer only once which promises low-latency implementations and it can avoid too short blocks according to our design
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