Book Description

Cochannel interference (CCI) and intersymbol interference (ISI) degrade the performance of cellular communication systems. In this dissertation, we investigate adaptive beamforming algorithms to suppress CCI in time-division multiple-access (TDMA) systems. In one approach, we are interested in iterative weight refinement techniques where the beamformer weights are computed successively using re-encoded data. We also suggest a semi-blind algorithm using a modified least-squares (LS) cost function. In addition, adaptive filtered training sequences compensate for the sampling phase offset of cochannel signals. At low signal-to-interference ratios (SIRs), the performance of the semi-blind beamformer exceeds that of the beamformer using filtered training sequences; however, for high SIRs, the reverse occurs. In order to improve the beamformer performance without regard to SIRs, we propose a hybrid beamformer using re-encoded data, filtered training sequences, and the semi-blind algorithm. Furthermore, we extend these beamforming algorithms to channel equalizers to mitigate ISI. We also analyze their performance using a stochastic model based on Wiener filter theory. Finally, we evaluate various receivers incorporating the proposed beamformers and equalizers. In these receivers, the TDMA data are processed in several stages that include the following series of steps: frame synchronization, adaptive beamforming, signal demodulation, adaptive equalization, and decoding. The performance of the multistage receivers is evaluated using simulated and real TDMA data.