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Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform

Sebastian Cammerer, Benedikt Leible, Matthias Stahl, Jakob Hoydis, Stephan ten Brink
Institute of Telecommunications, Pfaffenwaldring 47, University of Stuttgart, 70569 Stuttgart, Germany
arXiv:1609.09358 [cs.IT], (29 Sep 2016)

@article{cammerer2016combining,

   title={Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform},

   author={Cammerer, Sebastian and Leible, Benedikt and Stahl, Matthias and Hoydis, Jakob and Brink, Stephan ten},

   year={2016},

   month={sep},

   archivePrefix={"arXiv"},

   primaryClass={cs.IT}

}

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The decoding performance of polar codes strongly depends on the decoding algorithm used, while also the decoder throughput and its latency mainly depend on the decoding algorithm. In this work, we implement the powerful successive cancellation list (SCL) decoder on a GPU and identify the bottlenecks of this algorithm with respect to parallel computing and its difficulties. The inherent serial decoding property of the SCL algorithm naturally limits the achievable speed-up gains on GPUs when compared to CPU implementations. In order to increase the decoding throughput, we use a hybrid decoding scheme based on the belief propagation (BP) decoder, which can be intraand inter-frame parallelized. The proposed scheme combines excellent decoding performance and high throughput within the signal-to-noise ratio (SNR) region of interest.
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