Analysis and optimization of auto-correlation based frequency offset estimation

I. M. Ngebani; J. M. Chuma; S. Masupe

Analysis and optimization of auto-correlation based frequency offset estimation

I. M. Ngebani, J. M. Chuma, S. Masupe

Electrical, Computer & Telecommunications Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

In this letter, a general auto-correlation based frequency offset estimation (FOE) algorithm is analyzed. An approximate closed-form expression for the Mean Square Error (MSE) of the FOE is obtained, and it is proved that, given training symbols of fixed length N, choosing the number of summations in the auto-correlation to be (Formula presented.) and the correlation distance to be (Formula presented.) is optimal in that it minimizes the MSE. Simulation results are provided to validate the analysis and optimization.

Original language	English
Pages (from-to)	162-167
Number of pages	6
Journal	SAIEE Africa Research Journal
Volume	106
Issue number	3
Publication status	Published - Sep 1 2015

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

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abstract = "In this letter, a general auto-correlation based frequency offset estimation (FOE) algorithm is analyzed. An approximate closed-form expression for the Mean Square Error (MSE) of the FOE is obtained, and it is proved that, given training symbols of fixed length N, choosing the number of summations in the auto-correlation to be (Formula presented.) and the correlation distance to be (Formula presented.) is optimal in that it minimizes the MSE. Simulation results are provided to validate the analysis and optimization.",

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AU - Chuma, J. M.

AU - Masupe, S.

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N2 - In this letter, a general auto-correlation based frequency offset estimation (FOE) algorithm is analyzed. An approximate closed-form expression for the Mean Square Error (MSE) of the FOE is obtained, and it is proved that, given training symbols of fixed length N, choosing the number of summations in the auto-correlation to be (Formula presented.) and the correlation distance to be (Formula presented.) is optimal in that it minimizes the MSE. Simulation results are provided to validate the analysis and optimization.

AB - In this letter, a general auto-correlation based frequency offset estimation (FOE) algorithm is analyzed. An approximate closed-form expression for the Mean Square Error (MSE) of the FOE is obtained, and it is proved that, given training symbols of fixed length N, choosing the number of summations in the auto-correlation to be (Formula presented.) and the correlation distance to be (Formula presented.) is optimal in that it minimizes the MSE. Simulation results are provided to validate the analysis and optimization.

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ER -

Analysis and optimization of auto-correlation based frequency offset estimation

Abstract

All Science Journal Classification (ASJC) codes

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