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arXiv 1609.09358cs.ITmath.IT
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基于GPU平台的极化码置信传播和逐次消去列表译码

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

摘要:

极化码的解码性能很大程度上取决于所使用的解码算法,而解码器吞吐量及其延迟也主要取决于解码算法。在这项工作中,我们实现了强大的连续取消列表(SCL)解码器的GPU上,并确定该算法的并行计算和它的困难的瓶颈。与CPU实现相比,SCL算法的固有串行解码属性自然限制了GPU上可实现的加速增益。为了提高解码吞吐量,我们使用一种混合解码方案的基础上的置信传播(BP)解码器,它可以在帧内和帧间并行。该方案结合了优良的解码性能和高吞吐量的信号噪声比(SNR)的区域内的利益。

英文摘要:

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 intra and inter-frame parallelized. The proposed scheme combines excellent decoding performance and high throughput within the signal-to-noise ratio (SNR) region of interest.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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