SNR estimation using extended kalman filter technique for orthogonal frequency division multiplexing (OFDM) system
Signal to Noise Ratio (SNR) estimation of a received signal is an important and essential information for Orthogonal Frequency Division Multiplexing (OFDM) system. This is because in OFDM system, robustness in frequency selective channels can be achieved using adaptable transmission parameters...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/2468/1/24p%20SYLVIA%20ONG%20AI%20LING.pdf http://eprints.uthm.edu.my/2468/2/SYLVIA%20ONG%20AI%20LING%20WATERMARK.pdf http://eprints.uthm.edu.my/2468/ |
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| Summary: | Signal to Noise Ratio (SNR) estimation of a received signal is an important and
essential information for Orthogonal Frequency Division Multiplexing (OFDM)
system. This is because in OFDM system, robustness in frequency selective channels
can be achieved using adaptable transmission parameters. Therefore, to reckon these
parameters, knowledge of SNR estimates obtained by channel state information is
required for optimal performance. The performance of SNR estimation algorithm is
contingent on channel estimates obtained through channel estimation schemes. In
this project, two estimators which are Least Square (LS) and Minimum Mean Square
Error (MMSE) estimators are simulated and analyzed. From the result obtained, LS
shows better performance than MMSE in terms of Symbol Error Rate (SER) and
Mean Square Error (MSE) via computer simulation. With different number of sub
carriers implemented for the system model, 16, 32, 64, the result apparently shows
that the SER curve of the estimator with the highest number of sub carriers, 64 is
significantly lower compare with the other estimators with sub carriers of 16 and 32.
Therefore, a system model which contribute to 64 sub carriers are implemented.
However, in case of wireless channels, they possess non linearity where the LS and
MMSE, linear estimators yield inefficient results. Therefore, to improve the SNR
estimation, an efficient non linear Extended Kalman Filter (EKF) estimation, is
implemented into the OFDM system. The EKF estimator outperforms the LS and
MMSE estimators in terms of SER and MSE for AWGN channel. The beauty of the
estimation is that it can estimate the past, present and future |
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