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A Novel Technique for Estimating Frequency Offset in Orthogonal Frequency Division Multiplexing

Abstract— Orthogonal Frequency Division Multiplexing (OFDM) is broadly considered as an effective approach for current and future high-speed wireless communication. However, one of the main drawbacks of the OFDM is sensitive to carrier frequency offset (CFO). The CFO causes interference among the multiplicity of carriers in the OFDM signal. Thus, CFO must be estimated and compensated to minimize adverse effects of inter-carrier interference on the signal and maintain orthogonality. Moose technique shows a novel approach to estimate CFO values that less than 0.5 using a repeated data symbol. It was successfully able to estimate accurate CFO values using a maximum likelihood estimation algorithm. Moose also describes how to estimate CFO values greater than 0.5 using shorter training symbols, but the accuracy of the CFO values have decresed. In this paper, we present a novel technique to estimate CFO values that are greater than 0.5 while maintaining their accuracy level. Our technique is used to estimate large range of CFO values using shorter training symbols and then determine a numerical multiplicand by a step function. Based on a numerical multiplicand, the CFO values will be reduced in order that the maximum likelihood estimation algorithm can be applied because the CFO values became less than 0.5. Consequently, the estimation performance is increased and the estimation range becomes wider. The analytical analysis shows better mean square error for our technique compared to Moose technique.

Index Terms— orthogonal frequency division multiplexing (OFDM), carrier frequency offset (CFO), maximum likelihood estimation (MLE).

Mustafa Altaha, Humor Hwang
Department of Information and Communications Engineering, Myongji University, KOREA

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Cite: Mustafa Altaha, Humor Hwang, "A Novel Technique for Estimating Frequency Offset in Orthogonal Frequency Division Multiplexing," Proceedings of 2016 6th International Workshop on Computer Science and Engineering, pp. 335-339, Tokyo, 17-19 June, 2016.