空时格码联合信道估计与译码

信号处理 Pub Date : 2001-08-06 DOI:10.1109/SSP.2001.955348

Jianqiu Zhang, P. Djuric

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引用次数: 3

摘要

本文讨论了时序重要抽样(SIS)在空时格码(STTC)联合信道估计和译码中的应用。首先，我们提出了系统的动态状态空间模型(DSSM)，然后简要回顾了SIS的理论。通过仿真表明，SIS的一种特殊情况，即SIS与卡尔曼滤波的结合是解决时变平坦衰落环境问题的一种可行方法。如果相位模糊可以避免，我们的解决方案是可以接受的。通过仔细设计STTC调制星座，可以减少相位模糊。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Joint channel estimation and decoding of space-time trellis codes

In this paper, we discuss the application of sequential importance sampling (SIS) to joint channel estimation and decoding of space-time trellis codes (STTC). First, we present the dynamic state space model (DSSM) of the system, and then we briefly review the theory of SIS. A special case of SIS, a combination of SIS and Kalman filtering, is shown through simulations to be a viable approach to the problem which addresses time-varying flat-fading environments. Our solution is admissible if phase ambiguity can be avoided. We show that the phase ambiguity can be reduced by carefully designing the STTC modulation constellations.

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来源期刊

信号处理

自引率

0.00%

发文量

5812

期刊介绍： Journal of Signal Processing is an academic journal supervised by China Association for Science and Technology and sponsored by China Institute of Electronics. The journal is an academic journal that reflects the latest research results and technological progress in the field of signal processing and related disciplines. It covers academic papers and review articles on new theories, new ideas, and new technologies in the field of signal processing. The journal aims to provide a platform for academic exchanges for scientific researchers and engineering and technical personnel engaged in basic research and applied research in signal processing, thereby promoting the development of information science and technology. At present, the journal has been included in the three major domestic core journal databases "China Science Citation Database (CSCD), China Science and Technology Core Journals (CSTPCD), Chinese Core Journals Overview" and Coaj. It is also included in many foreign databases such as Scopus, CSA, EBSCO host, INSPEC, JST, etc.