| Summary: | 碩士 === 嶺東科技大學 === 資訊科技系碩士班 === 105 === This thesis deals with blind carrier frequency offset (CFO) estimation based on swarm intelligence algorithms for interleaved orthogonal frequency division multiple access (OFDMA) uplink systems under one data block. With the centro-symmetric (CS) trimmed autocorrelation matrix and orthogonal projection (OP) algorithm, the implementations of the searching-based and polynomial rooting CSOP estimators are easy. But for the searching-based estimator, the complexity and estimation accuracy strictly depend on the grid size used during the search. It is time consuming and the search grid is not clear. In addition, in the presence of finite sample effect and noise, to find out the roots’ precise location becomes more ambiguously for the polynomial rooting method, especially in low signal-to-noise ratio. To improve estimate resolution and accuracy, the main idea of this thesis is to integrate particle swarm optimization (PSO) and gravitational search algorithm (GSA) to develop simple and efficient CFO estimators. First, based on signal structure of interleaved OFDMA, we can divide the whole possible CFO range into several smaller search ranges. Therefore, the developed evolutionary searching algorithms can conquer the effect of ambiguous peaks and reduce the probability of trapping in local optimum for search range. From reducing the computational complexity of spectrum search, this thesis includes three research topics. PSO is one of the most widely used evolutionary algorithms in hybrid methods due to its simplicity, convergence speed, and ability of searching global optimum. In the first topic, the PSO-based estimation technique of the CFO of multiple active users is addressed. PSO may converge to a local optimum solution and the performance of PSO highly depends on the internal parameters. However, it is not quite clear how to vary these parameters during the run to get the best tradeoff between exploration and exploitation. Due to the lack of knowledge of the searching process, it is very difficult to design a mathematical model to adapt the parameters dynamically. Therefore, this topic also develops PSO algorithm based on fuzzy inference systems to adjust searching ability of the particles for the global optimum and increase estimation performance. In the second topic, this thesis deals with CFO estimation based on GSA for interleaved OFDMA uplink systems. But, the particle in GSA has no memory ability. A new hybrid algorithm is introduced utilizing strengths of PSO and GSA in the third topic. The main idea is to integrate the abilities of PSO in exploitation and GSA in exploration. Finally, several computer simulation results are provided for illustrating the effectiveness of the proposed methods.
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