Time-delay estimation in cognitive radio and MIMO systems

In this thesis, the time-delay estimation problem is studied for cognitive radio systems, multiple-input single-output (MISO) systems, and cognitive single-input multiple-output (SIMO) systems. A two-step approach is proposed for cognitive radio and cognitive SIMO systems in order to perform time-delay estimation with significantly lower computational complexity than the optimal maximum likelihood (ML) estimator. In the first step of this two-step approach, an ML estimator is used for each receiver branch in order to estimate the unknown parameters of the signal received via that branch. Then, in the second step, the estimates from the first step are combined in various ways in order to obtain the final time-delay estimate. The combining techniques that are used in the second step are called optimal combining, signal-to-noise ratio (SNR) combining, selection combining, and equal combining. It is shown that the performance of the optimal combining technique gets very close to the Cramer-Rao lower bound (CRLB) at high SNRs. These combining techniques provide various mechanisms for diversity combining for time-delay estimation and extend the concept of diversity in communications systems to the time-delay estimation problem in cognitive radio and cognitive SIMO systems. Simulation results are presented to evaluate the performance of the proposed estimators and to verify the theoretical analysis. For the solution of the time-delay estimation problem in MISO systems, ML estimation based on a genetic global optimization algorithm, namely, differential evolution (DE), is proposed. This approach is proposed in order to decrease the computational complexity of the ML estimator, which results in a complex optimization problem in general. A theoretical analysis is carried out by deriving the CRLB. Simulation studies for Rayleigh and Rician fading scenarios are performed to investigate the performance of the proposed algorithm.