| Budget Amount *help |
¥3,730,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥330,000)
Fiscal Year 2007: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2006: ¥2,300,000 (Direct Cost: ¥2,300,000)
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| Research Abstract |
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
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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
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