基于LDPC的DSC中连续信源相关性建模的改进
Improved Modeling of the Correlation Between Continuous-Valued Sources in LDPC-Based DSC
摘要：
信源之间相关性的精确建模对分布式信源编码（DSC）系统的效率起着至关重要的作用。这种相关性通常在二进制域中通过使用单个二进制对称信道（BSC）来建模，对于二进制和连续值源都是如此。我们发现，“一个”BSC不能准确地捕捉连续值源之间的相关性，一个更准确的模型需要“多个”BSC，用于代表每个样本的位数一样多。我们将这种新的模式到DSC系统，使用低密度奇偶校验（LDPC）码的压缩。标准Slepian-Wolf LDPC解码器需要稍微修改，使得所有BSC的参数被集成在对数似然比（LLR）中。此外，使用交织器，属于不同位平面的数据被混洗以在二进制域中引入随机性。新系统具有与标准系统相同的复杂度和延迟。仿真结果证明了所提出的模型和系统的有效性。
英文摘要：
Accurate modeling of the correlation between the sources plays a crucial role in the efficiency of distributed source coding (DSC) systems. This correlation is commonly modeled in the binary domain by using a single binary symmetric channel (BSC), both for binary and continuous-valued sources. We show that "one" BSC cannot accurately capture the correlation between continuous-valued sources; a more accurate model requires "multiple" BSCs, as many as the number of bits used to represent each sample. We incorporate this new model into the DSC system that uses low-density parity-check (LDPC) codes for compression. The standard Slepian-Wolf LDPC decoder requires a slight modification so that the parameters of all BSCs are integrated in the log-likelihood ratios (LLRs). Further, using an interleaver the data belonging to different bit-planes are shuffled to introduce randomness in the binary domain. The new system has the same complexity and delay as the standard one. Simulation results prove the effectiveness of the proposed model and system.
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