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Real-Time Signal-To-Noise Ratio Estimation Techniques for Use in Turbo Decoding

Real-Time Signal-To-Noise Ratio Estimation Techniques for Use in Turbo Decoding. Javier Schlömann and Dr. Noneaker. Motivation For Research. Turbo codes: high-performance error-correcting codes that achieve nearly-optimal performance over Gaussian noise channels

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Real-Time Signal-To-Noise Ratio Estimation Techniques for Use in Turbo Decoding

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  1. Real-Time Signal-To-Noise Ratio Estimation Techniques for Use in Turbo Decoding Javier Schlömann and Dr. Noneaker

  2. Motivation For Research • Turbo codes: high-performance error-correcting codes that achieve nearly-optimal performance over Gaussian noise channels • Signal-to-Noise Ratio (SNR): measure of received signal energy to noise power density • For optimum performance, turbo decoders need accurate knowledge of the SNR

  3. Purpose of Research • Analyze existing SNR estimation techniques • Devise, analyze, and compare other SNR estimation techniques that can be implemented into turbo decoders

  4. n(t) (b0, b1, …) Data Modulator t = (i+1)T Matched Filter v(t) Z i s(t) r(t) The Communications System • s(t) – transmitted binary antipodal modulated signal • n(t) – additive white Gaussian noise process • v(t) – filter with impulse response matched to the transmitted signal • Zi = matched filter output sampled at time (i+1)T

  5. General Transmission Formats Our focus (two special cases): • SNR estimation using data-modulated signal • Bit polarities, bi, are unknown a priori at receiver • SNR estimation using training sequence • Bit polarities, bi, are known a priori at receiver Training Seq. Training Seq. Data… Data

  6. Estimation Using Data Signal • Relationship between statistics and SNR • Estimate SNRusing empiricalapproximationto ratio

  7. Estimator Performance

  8. Two Alternative Estimators • Minimizes sensitivity to z offset • Easy to implement • No table look-up required

  9. Approximation 2 Performance

  10. Training Sequence Estimation

  11. Hybrid Technique • Uses minimum of data-signal and training sequence SNR estimations • Results for 13-bit training sequence:

  12. Conclusions • Without Training Sequence • Use Approximation 2 • With Training Sequence • Use Max SNR Limiting Estimator • Hybrid Estimator • Best overall performance

  13. Future Work • SNR estimation for channel with atime-varying SNR • Estimating SNR in multi-path channels • SNR estimate is useful in some equalization techniques

  14. Any Questions?

  15. Estimation Limiting Max SNR

  16. Real-Time Signal-To-Noise Ratio Estimation Techniques for Use in Turbo Decoding Javier Schlömann and Dr. Noneaker

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