Blind channel identification/equalization with applications in wireless communications

Abstract

The rapid growth in demand for cellular communications services has encouraged researchinto the design of wireless communications to improve spectrum eA?ciency and link quality.As opposed to their wireline counterpart, wireless communication systems pose some uniquechallenges. One of the main problems faced in wireless communications is the intersymbolinterference (ISI) introduced from channel dispersion and the multiuser interference (MUI)resulted from frequency reuse. In order to recover the desired transmitted user signals accurately, advanced space-time signal processing techniques for channel estimation need to be developed to simultaneously suppress the ISI and MUI. This dissertation focuses on the blind estimation of the wireless channels by exploiting the statistical information of the received data. We have developed a variety of statistics-based blind channel estimation methods for different data models, i.e., single-input single-output(SISO), single-input multiple-output (SIMO) and multiple-input multiple-output (MIMO)models. These proposed algorithms can be directly applied or tailored to diverse wireless communication systems, such as TDMA and CDMA, to combat the ISI and MUI whichconstitute a major impediment to the system performance.