Modeling and Analysis of Joint Decoding of Language-Based Sources with Polar Codes
Abstract: We consider a joint source-channel decoding problem where the source encoder leaves residual redundancy. Polar codes are considered in this framework and we show that the rate of polar codes can be improved by the natural redundancy in data. The improvement in rate depends on the distribution of frozen bits within a codeword. We propose an optimal information-bit allocation algorithm and analyze the maximum rate improvement with the proposed algorithm.
Bio: Ying Wang received her B.S. degree in Electrical Engineering from Beijing Jiaotong University, Beijing, China in 2010. From 2010 to 2012, she was a research assistant in Chinese Academy of Sciences, Beijing, China. Since 2012, she has been pursuing her Ph.D. degree in Electrical and Computer Engineering at Texas A&M University. Her research interests are in the areas of coding theory, data storage and wireless communications.