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

盲信号处理（BSP）是信号处理领域的新兴领域之一，具有一定的理论基础和广泛的应用前景。事实上，BSP已经成为许多领域研究和发展的一个非常重要的课题，特别是在移动通信、声学和语音处理以及生物医学工程等领域。BSP技术主要不使用任何训练数据，也不假设对瞬时混合或卷积混合系统的参数有先验知识。在本研究中，我们研究了卷积混合的盲信号（或源）分离（BSS）问题，并考虑了BSS技术在下一代移动通信中的应用。我们提出了几种BSS程序，并通过数字仿真实验研究了所提出程序的有效性。本文提出的方法大致可分为以下四类：(1)自适应超指数方法：在线BSS方法。(2)鲁棒超指数过程：噪声环境下的离线BSS技术。(3)带有参考信号的特征向量程序：离线BSS技术，BSS成功率非常高。(4)参考信号的鲁棒特征向量处理：噪声环境下的离线BSS技术，BSS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于1994年提出的单输入-单输出（SISO）系统的参考信号特征向量法扩展到多输入-多输出（MIMO）系统。对于(4)，我们将上述特征向量方法(3)扩展到噪声环境下。通过以上四种BSS程序的发展，我们将为下一代移动通信中的BSS建立理论基础。我们相信，理论基础为我们设计下一代移动通信中先进的源检索（或均衡器）提供了一个原则。少

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. 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 four categories:(1) Adaptive super-exponential procedures: On-line BSS … More 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.(4) Robust Eigenvector procedures with reference signals: Off-line BSS techniques in noisy environments 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. As for (4), we extended the above eigenvector method (3) the case in noisy environments.Through the developments of the above four 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