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机构地区:[1]华南理工大学电子与信息学院,广东广州510640
出 处:《华南理工大学学报(自然科学版)》2009年第9期42-46,共5页Journal of South China University of Technology(Natural Science Edition)
基 金:国家自然科学基金资助项目(60625101);国家自然科学青年基金资助项目(60502041);广东省自然科学基金博士启动项目(07300583)
摘 要:为充分利用信息采集网络中信源的相关性,有效解决采集设备的存储能力和计算能力受限的问题,提出了一种基于低密度校验码(LDPC)的分布式信源编码算法,将编码复杂度转移到解码端,在保证解码输出质量的基础上有效降低信源编码复杂度.该算法针对高斯信源,基于陪集分割原理,采用LDPC实现.实验结果表明:相对于网格码和Turbo码,该算法更加直观、简单,且在相关信噪比较低时具有明显的优势,当符号错误率为10^-3时,相对于网格码和Turbo码,信噪比分别有3.0-3.5 dB和0.7 dB左右的改进.In order to make full use of the correlations of sources in the information collection network,and to breakthrough the limitation of memory and computing capabilities of information-collecting equipment,a distributed source coding algorithm based on the low-density parity-check code(LDPC) is proposed,which transfers the co-ding complexity to the decoder and effectively reduces the source coding complexity without reducing the output quality of the decoder.The algorithm aims at Gaussian sources,implements with EDPC, and is based on the coset partition principle. Experimental results show that, as compared with Trellis and Turbo codes, the proposed algorithm is simpler and more intuitional, with obvious advantages in the case of low correlated signal-to-noise ratio, and that, at a symbol error rate of 10^-3, the signal-to-noise ratios increase by about 3.0 -3.5 dB and 0. 7 dB, respectively.
分 类 号:TN911.21[电子电信—通信与信息系统]
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