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 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
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Analysis and optimization of auto-correlation based frequency offset estimation. / Ngebani, I. M.; Chuma, J. M.; Masupe, S.
In: SAIEE Africa Research Journal, Vol. 106, No. 3, 01.09.2015, p. 162-167.Research output: Contribution to journal › Article
TY - JOUR
T1 - Analysis and optimization of auto-correlation based frequency offset estimation
AU - Ngebani, I. M.
AU - Chuma, J. M.
AU - Masupe, S.
PY - 2015/9/1
Y1 - 2015/9/1
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.
UR - http://www.scopus.com/inward/record.url?scp=84929312387&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929312387&partnerID=8YFLogxK
M3 - Article
VL - 106
SP - 162
EP - 167
JO - Transactions of the South African Institute of Electrical Engineers
JF - Transactions of the South African Institute of Electrical Engineers
SN - 0038-2221
IS - 3
ER -