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Development of Blind Signal Separation Technologies and their Application to Next-Generation Mobile Communications

盲信号分离技术的发展及其在下一代移动通信中的应用

基本信息

批准号：
16500033
负责人：
INOUE Yujiro
金额：
$ 2.24万
依托单位：
Shimane University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2004
资助国家：
日本
起止时间：
2004 至 2005
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-16500033/
关键词：
Blind Signal Processing Multiple Information Signal Equalizers Mobile Communications Inter-Symbol Interference Co-Channel Interference Code Division Multiple Access OFDM

项目摘要

Blind Signal Processing (BSP) is now one of emerging areas in signal processing with theoretical foundations and many potential applications. In fact, BSP has become a very important topic of research and developments in many areas, in particular, in mobile communications, acoustics and speech processing, and biomedical engineering. The BSP techniques principally do not use any training data and do not assume a priori knowledge about parameters of instantaneous mixing or convolutive mixing systems. In this research, we deal with the blind signal (or source) separation (BSS) problem for convolutive mixtures with taking the application of BSS techniques to next-generation mobile communications in consideration. We proposed several procedures for BSS and investigate the effectiveness of the proposed procedures through digital simulation experiments.Roughly speaking, the proposed procedures are classified into the following three categories :(1)Adaptive super-exponential procedures : On-li … More ne BSS techniques in slowly time-varying environments.(2)Robust super-exponential procedures : Off-line BSS techniques in noisy environments.(3)Eigenvector procedures with reference signals : Off-line BSS techniques with a very high success rate of BSS.As for (1) and (2) above, although we proposed the original (multi-channel) super-exponential methods in 2000,we proposed an adaptive version of the original ones, which can be utilized in slowly time-varying environments. As for (3),in connection to the super-exponential methods, we extended the eigenvector method with reference signals for single-input-single-output (SISO) systems proposed by B.Jelonnek and K.D.Kammeyer in 1994 to the case for multi-input-multi-output (MIMO) systems. Our research group will investigate the eigenvector approach with reference signals further in future.Through the developments of the above three types of BSS procedures, we will establish a theoretical foundation for BSS in next-generation mobile communications. We believe that the theoretical foundation gives us a principle for designing advanced source retrievers (or equalizers) in next-generation mobile communications. Less

盲信号处理（BSP）是信号处理领域的一个新兴领域，有着广泛的理论基础和应用前景。事实上，BSP已经成为许多领域研究和开发的一个非常重要的主题，特别是在移动的通信、声学和语音处理以及生物医学工程中。BSP技术原则上不使用任何训练数据，并且不假设关于瞬时混合或卷积混合系统的参数的先验知识。本研究针对卷积混合信号的盲信号（或源）分离（BSS）问题，并考虑BSS技术在下一代移动的通信中的应用。我们提出了几种盲源分离方法，并通过数字仿真实验研究了所提出方法的有效性。粗略地说，所提出的方法可分为以下三类：（1）自适应超指数方法：On-li ...更多信息新的BSS技术在缓慢时变的环境。（2）稳健的超指数过程：噪声环境中的离线BSS技术。（3）带参考信号的特征向量方法：离线盲源分离技术，盲源分离成功率非常高。对于上述（1）和（2），虽然我们在2000年提出了原始的（多通道）超指数方法，但我们提出了一种原始方法的自适应版本，可以在慢时变环境中使用。对于（3），结合超指数方法，我们将B.Jelonnek和K. D. Kammeyer在1994年提出的单输入单输出（SISO）系统的带参考信号的特征向量方法推广到多输入多输出（MIMO）系统的情况。本课题组将进一步研究基于参考信号的特征向量盲源分离方法，通过以上三种盲源分离方法的研究，为下一代移动的通信中的盲源分离奠定理论基础。我们相信，该理论基础为我们设计下一代移动的通信中先进的源取回器（或均衡器）提供了一个原则。少