基于资源Scalability的网络多描述估计与压缩
Networked Multiple Description Estimation and Compression with Resource Scalability
摘要：
我们提出了一种用于资源受限网络通信的联合源信道多描述（JSC-MD）框架（例如，传感器网络），其中一个或多个被剥夺的编码器将针对比特错误和擦除错误的马尔可夫源传送到许多异构解码器，一些是强大的，一些是被剥夺的。为了保持编码器的复杂度在最低限度，源编码成K个描述的一个简单的多描述量化器（MDQ）既没有熵也没有信道编码。解码器利用MDQ的码分集和网络的路径分集来纠正传输错误并提高编码效率。一个关键的设计目标是资源的可扩展性：强大的节点在网络中可以执行JSC-MD分布式估计/解码的最大后验概率（MAP）或最小均方误差（MMSE）的标准下，而原始节点诉诸于更简单的MD解码，所有工作与相同的MDQ码。应用JSC-MD的传感器网络中的隐马尔可夫模型的分布式估计。提出的JSC-MD MAP估计器是一种加权有向无环图中最长路径的算法，而JSC-MD MMSE解码器是著名的前向-后向算法到多描述的扩展。这两种算法同时利用源存储器，冗余的固定速率MDQ，和描述间的相关性。它们的性能优于现有的硬判决MDQ解码器的大幅度（高达8dB）。对于高斯马尔可夫信源，JSC-MD分布式MAP序列估计算法的复杂度可以降低到典型的单描述Viterbi类算法的复杂度。
英文摘要：
We present a joint source-channel multiple description (JSC-MD) framework for resource-constrained network communications (e.g., sensor networks), in which one or many deprived encoders communicate a Markov source against bit errors and erasure errors to many heterogeneous decoders, some powerful and some deprived. To keep the encoder complexity at minimum, the source is coded into K descriptions by a simple multiple description quantizer (MDQ) with neither entropy nor channel coding. The code diversity of MDQ and the path diversity of the network are exploited by decoders to correct transmission errors and improve coding efficiency. A key design objective is resource scalability: powerful nodes in the network can perform JSC-MD distributed estimation/decoding under the criteria of maximum a posteriori probability (MAP) or minimum mean-square error (MMSE), while primitive nodes resort to simpler MD decoding, all working with the same MDQ code. The application of JSC-MD to distributed estimation of hidden Markov models in a sensor network is demonstrated. The proposed JSC-MD MAP estimator is an algorithm of the longest path in a weighted directed acyclic graph, while the JSC-MD MMSE decoder is an extension of the well-known forward-backward algorithm to multiple descriptions. Both algorithms simultaneously exploit the source memory, the redundancy of the fixed-rate MDQ, and the inter-description correlations. They outperform the existing hard-decision MDQ decoders by large margins (up to 8dB). For Gaussian Markov sources, the complexity of JSC-MD distributed MAP sequence estimation can be made as low as that of typical single description Viterbi-type algorithms.
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