PARTICLE FILTERING ESTIMATION APPROACH IN ADVANCED DIGITAL COMMUNICATION SYSTEMS
The ever-increasing volume of users and the demand for more communication services bring about many advanced modulation and demodulation technologies which are developed to increase the spectrum efficiency and cope with challenging transmission conditions in digital communications. However, it is difficult to improve the performance of those traditional modulation and demodulation approaches without increasing transmit power and lowering spectrum efficiency. This thesis studies the application of powerful Particle Filtering methods to the problems associated with the interference cancellation, equalization, demodulation, and decoding of the signals over communication channel. In this thesis, theoretic models of using particle filtering approaches in digital communications are investigated, and several specific algorithms and schemes are considered as applications of the theoretic models. First, the application of particle filtering in delayed decision feed-back sequence estimation equalization is addressed. The particle filtering approach is then introduced to an efficient particle filtering receiver for inter-carrier interference cancellation and demodulation of M-ary modulated signals in OFDM/OFDMA system under time-variant Rayleigh fading channels. Subsequently, an efficient sequential Monte Carlo (SMC) demodulation approach for Polynomial Phase Modulation (PPM) is discussed. The interference cancellation and demodulation algorithm for MIMO-PPM scenario is then derived. The analysis of performance and computational complexity for SMC particle filtering approach is also provided. Comprehensive simulation results confirm that the proposed sequential Monte Carlo particle filtering approaches have better performance than the conventional methods.