| Summary: | 碩士 === 嶺東科技大學 === 資訊科技系碩士班 === 107 === This thesis deals with the blind carrier frequency offset (CFO) based on the swarm intelligence (SI) optimization algorithms with the weighted subspace fitting (WSF) criterion for interleaved frequency division multiplexing access (OFDMA) uplink system. For the CFO estimation problem, it is well know that the WSF has superior statistical characteristics and better estimation performance. However, this the type of CFO estimation must pass through the high dimensional space problem. Optimizing complex nonlinear multi-modal functions requires a large computational load, which makes it seem difficult and not easy to maximize or minimize nonlinear objective functions in large parameter spaces. Therefore, this thesis uses the SI optimization algorthms to improve the estimation accuracy and reduce computational load. The main optimization algorithms include particle swarm optimization (PSO), gravitational search algorithm (GSA), the hybrid PSO and GSA (PSOGSA), and the whale optimization algorithm (WOA). Meanwhile, this thesis also adds the fuzzy inference system to PSO and GSA for reducing the required number of iterations. Finally, several simulation results are provided for illustrating the effectiveness of the proposed CFO estimators.
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