A non-data-aided algorithm based on ML for OFDM synchronization

Orthogonal frequency division multiplexing (OFDM) is more sensitive to symbol timing offset (STO) and carrier frequency offset (CFO) than single-carrier signals, which requires more accurate synchronization algorithms. In this paper, the traditional ML synchronization algorithm is improved by accumu...

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Vydáno v:	2018 International Conference on Electronics Technology (ICET) s. 1 - 6
Hlavní autoři:	An, Jiancheng, Gan, Lu, Liao, Hongshu
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.05.2018
Témata:	CFO Correlation coefficient Maximum likelihood estimation OFDM Signal to noise ratio STO Synchronization
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Orthogonal frequency division multiplexing (OFDM) is more sensitive to symbol timing offset (STO) and carrier frequency offset (CFO) than single-carrier signals, which requires more accurate synchronization algorithms. In this paper, the traditional ML synchronization algorithm is improved by accumulating multiple OFDM symbols, and joint estimation of symbol timing offset and carrier frequency offset is accomplished without data aiding. The simulation illustrates that the improved ML algorithm has a higher accuracy for STO estimation and a lower MSE for CFO estimation.
DOI:	10.1109/ELTECH.2018.8401408