基于高斯源的迷失式压缩的信念传播优化
Huan Deng1,2, Dan Song1, Zhiping Xu3,4
1Navigation Collage, Jimei University, Xiamen 361021, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
本研究介绍了优化的信念传播 (BP) 算法,用于物联网中的传感器数据的高效损耗压缩. 这些算法通过减轻与低密度平价检查代码中的捕获集相关的问题来提高数据压缩性能.
科学领域:
- 信息理论 信息理论
- 数据压缩数据压缩
- 信号处理 信号处理
背景情况:
- 物联网 (IoT) 传感器节点需要高效的数据压缩来进行存储.
- 损耗压缩对于处理连续的环境数据流至关重要.
- 现有的压缩方案可以改进以提高效率.
研究的目的:
- 为连续数据源开发高效的损耗压缩技术.
- 应用损失源代码和通道解码之间的二元原则.
- 为高斯源引入和优化信念传播 (BP) 算法.
主要方法:
- 实现基于二元原则的损失源代码.
- 使用信念传播 (BP) 算法对高斯源进行压缩.
- 提出了优化的BP算法,以解决在原型机低密度平价检查 (P-LDPC) 代码中的捕获集引起的性能限制.
主要成果:
- 优化的BP算法有效地减弱了捕获套件的影响.
- 使用拟议的算法实现了更好的扭曲率性能.
- 模拟结果验证了增强的压缩效率.
结论:
- 优化的BP算法为物联网应用中的丢失源代码提供了卓越的性能.
- 双重性原理为高效的数据压缩提供了一个新的框架.
- 缓解捕获设置效应是增强基于BP的压缩的关键.
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