Blind adaptive cyclic filtering and beamforming algorithms

Abstract

<p>In a multi-user communication system such as the wireline or wireless communication systems, a commonly encountered problem is the extraction of the desired signal from Co-Channel Interference (CCI) and Adjacent Channel Interference (ACI). To combat the CCI and ACI, the conventional filtering techniques are unable to carry out the job. The optimum FREquency-SHift (FRESH) filtering technique proposed by W. A. Gardner enables us to suppress spectrally overlapped signals by using the cyclostationarity of the signals. However, to design the optimum FRESH filter, we must have the statistical knowledge of the desired signal or a training signal which, in practice, are not often available. This thesis proposes a blind adaptive FRESH filtering algorithm which does not need a training signal to extract the desired signal from spectrally overlapping interference. We call this new technique Blind Adaptive (BA)-FRESH filtering. Comparing the BA-FRESH filter with the FRESH filter with a training signal which is called Trained Adaptive FRESH (TA-FRESH) filter, it has been proved that BA-FRESH and TA-FRESH have same performances when the data length is infinite. On the other hand, various cyclic beamforming techniques such as the spectral Self-COherence REstoral (SCORE), the Cyclic Adaptive Beamforming (CAB), the Constrained Cyclic Adaptive Beamforming (C-CAB) and the Robust Cyclic Adaptive Beamforming (R-CAB) algorithms can be used to combat CCI and ACI efficiently. However, when the desired signal and the interferences are very closely spaced in arrival directions, system performance improvement using these cyclic beamforming alone is limited because the beamformers are just spatial filters. By combining the spatial beamforming with the temporal FRESH filtering, a large system performance improvement may be achieved due to the full utilization of the signal information in both time and space domains. A Blind Adaptive Space-Time (BLAST) algorithm is proposed in this thesis. The BLAST algorithm is a blind adaptive time varying space-time filter. The BLAST algorithm can be viewed as the expansion of the BA-FRESH filtering algorithm to the space-time domain. Comparing the BLAST filter with the space-time filter with a training signal which is called Trained Adaptive Space-Time (TAST) filter, it has been proved that BLAST and TAST have same performances when the data length is infinite. When the data length is finite, there are performance differences between BLAST and TAST. Convergence of the BLAST and TAST filter coefficients, the finite sample output signal to interference plus noise ratio (SINR), and the finite sample output mean square error (MSE) are analyzed. (Abstract shortened by UMI.)</p>