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首页> 外文期刊>IEEE Signal Processing Magazine >Sparse Graph Codes for Side Information and Binning
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Sparse Graph Codes for Side Information and Binning

机译：附带信息和装箱的稀疏图形代码

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The pioneering work of Shannon provides fundamental bounds on the rate limitations of communicating information reliably over noisy channels (the channel coding problem), as well as the compressibility of data subject to distortion constraints (the lossy source coding problem). However, Shannon's theory is nonconstructive in that it only establishes the existence of coding schemes that can achieve the fundamental bounds but provides neither concrete codes nor computationally efficient algorithms. In the case of channel coding, the past two decades have witnessed dramatic advances in practical constructions and algorithms, including the invention of turbo codes and the surge of interest in low-density parity check (LDPC) codes. Both these classes of codes are based on sparse graphs and yield excellent error-correction performance when decoded using computationally efficient methods such as the message-passing sum-product algorithm. Moreover, their performance limits are well characterized, at least in the asymptotic limit of large block lengths, via the density evolution method.
机译：Shannon的开创性工作为在嘈杂的信道上可靠地传递信息的速率限制（信道编码问题）以及受失真约束的数据可压缩性（有损源编码问题）提供了基本界限。但是，香农的理论是非建设性的，因为它仅建立了可以实现基本范围的编码方案，而既没有提供具体的代码，也没有提供有效的算法。在信道编码的情况下，过去的二十年见证了实际结构和算法的巨大进步，包括turbo码的发明以及对低密度奇偶校验（LDPC）码的兴趣激增。这两类代码均基于稀疏图，并且使用诸如消息传递和积算法之类的高效计算方法进行解码时，可产生出色的纠错性能。此外，通过密度演化方法，至少在大块长度的渐近极限中，可以很好地表征其性能极限。

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