International Journal of Future Generation Communication and Networking Vol. 4, No. 4, December, 2011 1 Novel SNR Estimation Teachnique In Wireless OFDM Systems Shahid Manzoor, Varun Jeoti, Nidal Kamel and Muhammad Asif Khan Universiti Teknologi Petronas (UTP), Malaysia [email protected], [email protected], [email protected], [email protected]Abstract A novel front-end noise power and SNR estimation technique based on one OFDM preamble is proposed and compared with previously published SNR estimators – none of which are front-end estimators. This paper is extended and expanded version of our previous work. It provides complete mathematics and discussion of designed algorithms for front end SNR estimation. The proposed technique is divided into two parts. In the first part, SNR estimation technique for AWGN channel and wireless multipath channels is considered. In the second part, the proposed estimator takes into consideration the different noise power levels over the OFDM sub-carriers. The OFDM band is divided into several sub-bands using wavelet packet decomposition and noise in each sub-band is considered white. The second- order statistics of the transmitted OFDM preamble are calculated in each sub-band and the noise power is estimated. The proposed estimator in first part is compared with Reddy estimator and Subspace based estimator for AWGN channel in terms of normalized mean square error and in the second part it is compared with Reddy’s estimator for colored noise in terms of mean square error (MSE). It is observed that current estimator gives better SNR estimates than Reddy and subspace estimators and can estimate local statistics of the noise power when the noise is colored. Keywords: Noise power estimator, SNR estimation, Adaptive modulation, OFDM 1. Introduction Noise variance and hence SNR estimates of the received signal are very important parameters for the channel quality control in communication systems [1]. The search for a good SNR estimation technique is motivated by the fact that various algorithms require knowledge of the SNR for optimal performance. For instance, in OFDM systems, SNR estimation is used for power control, adaptive coding and modulation, turbo decoding etc. [1]-[4]. Many SNR estimation algorithms have been suggested in the last ten years [5], [6], [7] and successfully implemented in OFDM systems at the back-end of receiver using the system pilot symbols. Nidal et al. in [8] proposed linear prediction based SNR estimation for AWGN channel at front-end for single carrier systems. Many SNR estimators in digital communication channels have been proposed over the last few decades [9]. Most of these techniques derive the symbol SNR estimates solely from the received signal at the output of the matched filter (MF). The estimators assume perfect carrier and symbol synchronization while at the same time implicitly assuming intersymbol interference (ISI)-free output of the MF (the decision variable). However, in practice, multipath wireless communication gives rise to much intersymbol interference, especially in indoor and urban areas. In these ISI dominated scenarios, SNR estimators that do not
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Novel SNR Estimation Teachnique In Wireless OFDM Systems · SNR estimator for the white noise as well as for colored noise in OFDM system is proposed [18, 19]. The algorithm is based
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International Journal of Future Generation Communication and Networking
Vol. 4, No. 4, December, 2011
1
Novel SNR Estimation Teachnique In Wireless OFDM Systems
Shahid Manzoor, Varun Jeoti, Nidal Kamel and Muhammad Asif Khan
Complexity and Accuracy: The results shows that the proposed estimator fulfill the criteria
of good SNR estimator.The proposed estimator has relatively low computational complexity
and easy to implement because it makes use of only one OFDM preamble signal to find the
SNR estimates unlike all previous SNR estimators. It provides accurate SNR estimates as the
estimation error is less than 0.2dB overall. It is computationally fast because there is no need
of averaging over many OFDM symbols to get accurate SNR estimates as compared to
previous SNR estimators.
Fig.8 Comparison of part-one’s Fig.9 Comparison of part one’s
proposed method proposed method
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Vol. 4, No. 4, December, 2011
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2 4 6 8 10 12 14 16 18 200
0.05
0.1
0.15
0.2
0.25
0.3
SNR dB
SN
R-N
MS
E
Comparison of different channel's NMSE with proposed method
AWGN
Rayleigh 3 Tap
Rayleigh 5 Tap
Rayleigh 10 Tap
Rician 3 Tap
Rician 5 Tap
Rician 10 Tap
.
Fig. 10 Comparison of different channel’s NMSE with part one’s
proposed method.
Fig.11 Comparison of different channel’s est SNR with fist part of
proposed method
Fig.12 Mean-square-error performance of the proposed
technique in 2nd part with other
algorithms in colored noise.
Fig.13 Actual SNR vs. global SNR estimates of colored noise with proposed technique in 2nd -part
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1 5 10 15 20 25 30 35 400
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05
Numbers of OFDM symbols
SN
R N
MS
E
SNR = 5 dB
SNR = 10 dB
SNR = 15 dB
Fig.14 SNR NMSE for different values of average SNR (consistency check for
estimates by averaging)
5. Conclusion
There is no work done at front-end of the receiver for multicarrier systems like OFDM. In
contrast to other SNR estimators that derive SNR estimates at the back-end of the receiver, a
novel SNR estimator is presented which can operate at the front-end of the receiver. Proposed
estimator makes use of one OFDM symbol to estimates the SNR unlike all previous
estimators which makes use of many OFDM symbols to get SNR estimates. In the first part
of proposed technique noise is assumed to be white and SNR estimation is done over all
OFDM symbol. In the second part the assumption of the noise to be white is removed. Also,
variation of the noise power across OFDM sub-carriers is allowed. Therefore, the proposed
approach estimates both local (within smaller sets of subcarriers) and global (over all sub-
carriers) SNR values. The short term local estimates calculate the noise power variation
across OFDM sub-carriers. These estimates are specifically very useful for adaptive
modulation, and optimal soft value calculation for improving channel decoder performance.
The performance of proposed technique has been evaluated via computer simulations and
implemented in OFDM systems. The results show that the current estimator performs better
than other conventional methods. Complexity to find SNR estimates is much lower because
the current estimator makes use of only one OFDM preamble signal. To check the
consistency of estimator, averaging SNR NMSE results for different values of average SNR
are taken and results shows the developed SNR estimation technique is provides very reliable
estimates of SNR.
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[3] T. Keller and L. Hanzo, “Adaptive orthogonal frequency division multiplexing schemes”, in proceedings of ACTS Mobile Communications Summit, June 1998, pp. 794-799.
[4] T.Keller and L.Hanzo, “Adaptive Multicarrier Modulation: A convenient framework for time-frequency processing in wireless communications”, in proceeding of IEEE, vol. 88, May 2000, pp. 611-640.
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[5] Reddy, S. and Arslan H. “Noise Power and SNR Estimation for OFDM Based Wireless Communication Systems”, Wireless Communication and Signal Processing Group, 2003
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Authors
Rana Shahid Manzoor is currently PhD student in Electrical &
Electronic department, Universiti Teknologi PETRONAS (UTP)
Malaysia and also working as research associate. He received his MS
degree in Electrical & Electronic Engineering from UTP in July-2008.
He worked as Teaching Research Assistant during his MS degree. He
received his BS degree in Computer Engineering (With Honors) from
University of Engineering and Technology Taxila (UET), Pakistan in
Jan-2005. He served 1 year as Teaching and Research Associate (TRA)
in UET Taxila, Pakistan. His research Focused on Wireless LAN and
MAN technologies; signal processing, SNR estimation, noise power
estimation and wavelet packets based OFDM systems, RFID and WSN
integration for environmental monitoring.
International Journal of Future Generation Communication and Networking
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Varun Jeoti received his Ph.D. degree from Indian Institute of
Technology Delhi India in 1992. He worked on several sponsored R&D
projects in IIT Delhi and IIT Madras during 1980 to 1989 developing