Distortion-Tolerant Communications with Correlated Information

This dissertation is devoted to the development of distortion-tolerant communication techniques by exploiting the spatial and/or temporal correlation in a broad range of wireless communication systems under various system configurations. Signals observed in wireless communication systems are often correlated in the spatial and/or temporal domains, and the correlation can be used to facilitate system designs and to improve system performance. First, the optimum node density, i.e., the optimum number of nodes in a unit area, is identified by utilizing the spatial data correlation in the one- and two-dimensional wireless sensor networks (WSNs), under the constraint of fixed power per unit area. The WSNs distortion is quantized as the mean square error between the original and the reconstructed signals. Then we extend the analysis into WSNs with spatial-temporally correlated data. The optimum sampling in the space and time domains is derived. The analytical optimum results can provide insights and guidelines on the design of practical WSNs. Second, distributed source coding schemes are developed by exploiting the data correlation in a wireless network with spatially distributed sources. A new symmetric distributed joint source-channel coding scheme (DJSCC) is proposed by utilizing the spatial source correlation. Then the DJSCC code is applied to spatial-temporally correlated sources. The temporal correlated data is modeled as the Markov chain. Correspondingly, two decoding algorithms are proposed. The first multi-codeword message passing algorithm (MCMP) is designed for spatially correlated memoryless sources. In the second algorithm, a hidden Markov decoding process is added to the MCMP decoder to effectively exploit the data correlation in both the space and time domains. Third, we develop distortion-tolerant high mobility wireless communication systems by considering correlated channel state information (CSI) in the time domain, and study the optimum designs with imperfect CSI. The pilot-assisted channel estimation mean square error is expressed as a closed-form expression of various system parameters through asymptotic analysis. Based on the statistical properties of the channel estimation error, we quantify the impacts of imperfect CSI on system performance by developing the analytical symbol error rate and a spectral efficiency lower bound of the communication system

Protograph LDPC Based Distributed Joint Source Channel Coding

该文提出一种基于原模图低密度奇偶校验(P-LDPC)码的分布式联合信源信道编译码系统方案。该方案编码端,分布式信源发送部分信息位及校验位以同时实; 现压缩及纠错功能;译码端,联合迭代信源信道译码的运用进一步发掘信源的相关性以获得额外的编码增益。此外,论文研究了所提方案在译码端未知相关性系数的; 译码算法。仿真结果表明,所提出的基于P-LDPC码的分布式联合信源信道编译码方案在外部迭代次数不大的情况可以获得较大的性能增益,并且相关性系数在; 译码端已知和未知系统性能基本相当。This paper proposes a Distributed Joint Source-Channel Coding (DJSCC); scheme using Protograph Low Density Parity Check (P-LDPC) code. In the; proposed scheme, the distributed source encoder sends some information; bits together with the parity bits to simultaneously achieve both; distributed compression and channel error correction. Iterative joint; decoding is introduced to further exploit the source correlation.; Moreover, the proposed scheme is investigated when the correlation; between sources is not known at the decoder. Simulation results indicate; that the proposed DJSCC scheme can obtain relatively large additional; coding gains at a relatively small number of global iterations, and the; performance for unknown correlated sources is almost the same as that; for known correlated sources since correlation can be estimated jointly; with the iterative decoding process.福建省自然科学基金; 国家自然科学基