标题： 联合信源信道向量量化用于压缩感知 显示英文标题

标题： Joint Source-Channel Vector Quantization for Compressed Sensing

摘要： 我们研究使用向量量化器（VQ）对压缩感知（CS）测量值进行联合信源信道编码（JSCC）。 我们开发了一个框架，以实现最优的JSCC方案，该方案能够对稀疏源的CS测量值进行编码和传输，并解码稀疏源信号。 为此，考虑了VQ的编码器-解码器对的最优设计，其中最优性通过最小化端到端均方误差（MSE）来实现。 我们推导了MSE性能的理论下界，并通过迭代算法提出了一种实用的编码器-解码器设计方案。 所得到的编码方案被称为针对信道的CS向量量化器，称为COVQ-CS。 为了应对COVQ-CS的编码复杂度问题，我们提出使用一种结构化量化器，即低复杂度多级VQ（MSVQ）。 我们为MSVQ推导了新的编码和解码条件，然后提出了一种称为针对信道的CS多级VQ的实用编码器-解码器设计算法，称为COMSVQ-CS。 通过仿真研究，我们将提出的方案与相关量化器进行了比较。 显示英文摘要

摘要： We study joint source-channel coding (JSCC) of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a framework for realizing optimum JSCC schemes that enable encoding and transmitting CS measurements of a sparse source over discrete memoryless channels, and decoding the sparse source signal. For this purpose, the optimal design of encoder-decoder pair of a VQ is considered, where the optimality is addressed by minimizing end-to-end mean square error (MSE). We derive a theoretical lower-bound on the MSE performance, and propose a practical encoder-decoder design through an iterative algorithm. The resulting coding scheme is referred to as channel- optimized VQ for CS, coined COVQ-CS. In order to address the encoding complexity issue of the COVQ-CS, we propose to use a structured quantizer, namely low complexity multi-stage VQ (MSVQ). We derive new encoding and decoding conditions for the MSVQ, and then propose a practical encoder-decoder design algorithm referred to as channel-optimized MSVQ for CS, coined COMSVQ-CS. Through simulation studies, we compare the proposed schemes vis-a-vis relevant quantizers.