Polar Coded Quantization for Distributed Source Coding
career value
223K/year
🤖 AI Summary
This work addresses the problem of distributed source coding for Gaussian sources under mean squared error distortion by proposing a Wyner-Ziv Polar-Coded Quantization (WZ-PCQ) scheme designed to approach the corner point of the Berger–Tung region. The approach integrates scalar quantization, dithering, modulo-lattice operations, and truncated Gaussian probabilistic shaping, and—uniquely—incorporates short-block-length multilevel 5G polar codes into the Wyner–Ziv framework to enable jointly optimized polar coding. In contrast to conventional methods that process each source block independently, WZ-PCQ achieves significantly lower aggregate distortion and provides a tighter approximation to the theoretical performance bound.
Application Category
📝 Abstract
Scalar quantization and probabilistic shaping are applied to the distributed source coding of Gaussian sources, with mean-square error distortion. A coding scheme with a modulo interval, dithering, and truncated Gaussian shaping is shown to achieve the corner points of the Berger-Tung region. The theory is illustrated by designing short-block-length multilevel 5G polar codes for Wyner-Ziv (WZ) polar coded quantization (PCQ). WZ-PCQ substantially reduces the total distortion compared to separate PCQ of the source blocks.
Problem
Research questions and friction points this paper is trying to address.
Distributed Source Coding
Gaussian Sources
Mean-Square Error Distortion
Wyner-Ziv
Polar Coded Quantization
Innovation
Methods, ideas, or system contributions that make the work stand out.
Polar codes
Distributed source coding
Probabilistic shaping
Wyner-Ziv coding
Quantization
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
