Stochastic Multiple Stream Decoding of Cortex Codes
Stochastic Multiple Stream Decoding of Cortex Codes
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DOI：
10.1109/tsp.2011.2138699
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发表时间：
2011-07
影响因子：
5.4
通讯作者：
M. Arzel;C. Lahuec;C. Jégo;W. Gross;Y. Bruned
中科院分区：
文献类型：
--
作者：
M. Arzel;C. Lahuec;C. Jégo;W. Gross;Y. Bruned
作为实现基于置信传播的前向纠错（FEC）解码器的最有效的解决方案之一，随机处理因此是在解决诸如Cortex码的新兴码的解码时值得考虑的方法。该码族提供具有大汉明距离的短分组码。不幸的是，它们的构造引入了许多隐藏变量，使得它们难以用实现和积算法的数字电路有效地解码。通过引入多个随机流，该方案解决了隐变量问题，从而使解码性能接近最优。此外，这种新的随机架构是更有效的复杂性，吞吐量比最近公布的随机解码器使用边缘或跟踪预测记忆。
Being one of the most efficient solutions to implement forward error correction (FEC) decoders based on belief propagation, stochastic processing is thus a method worthy of consideration when addressing the decoding of emerging codes such as Cortex codes. This code family offers short block codes with large Hamming distances. Unfortunately, their construction introduces many hidden variables making them difficult to be efficiently decoded with digital circuits implementing the Sum-Product algorithm. With the introduction of multiple stochastic streams, the proposed solution alleviates the hidden variables problem thus yielding decoding performances close to optimal. Morevover, this new stochastic architecture is more efficient in terms of complexity-throughput ratio compared to recently published stochastic decoders using either edge or tracking forecast memories.