Difference between revisions of "MPSK SNR Estimator"
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== Parameters == | == Parameters == | ||
− | '''Type''' - There are currently four implemented estimators. See https://www.gnuradio.org/doc/doxygen-3.6.4/group__snr__blk.html for more details. | + | '''Type''' - There are currently four implemented estimators. See [https://www.gnuradio.org/doc/doxygen-3.6.4/group__snr__blk.html here] for more details. |
# '''Simple''': A very simple SNR estimator that just uses mean and variance estimates of an M-PSK constellation. This esimator is quick and cheap and accurate for high SNR (above 7 dB or so) but quickly starts to overestimate the SNR at low SNR. | # '''Simple''': A very simple SNR estimator that just uses mean and variance estimates of an M-PSK constellation. This esimator is quick and cheap and accurate for high SNR (above 7 dB or so) but quickly starts to overestimate the SNR at low SNR. |
Revision as of 21:04, 12 March 2019
A block for computing SNR of a M-PSK signal (e.g., BPSK, QPSK, 8-PSK). It copies the input stream to the output stream, but adds a tag that contains the estimated SNR every N samples. It may work with other types of signals, but with a certain amount of error. See each Type for how it works under the hood.
Parameters
Type - There are currently four implemented estimators. See here for more details.
- Simple: A very simple SNR estimator that just uses mean and variance estimates of an M-PSK constellation. This esimator is quick and cheap and accurate for high SNR (above 7 dB or so) but quickly starts to overestimate the SNR at low SNR.
- Skewness: SNR Estimator using skewness correction. This is an estimator that came from a discussion between Tom Rondeau and fred harris with no known paper reference. The idea is that at low SNR, the variance estimations will be affected because of fold-over around the decision boundaries, which results in a skewness to the samples. We estimate the skewness and use this as a correcting term. Best used with SNRs above 5 dB.
- 2nd and 4th Moment: An SNR estimator for M-PSK signals that uses 2nd (M2) and 4th (M4) order moments. This estimator uses knowledge of the kurtosis of the signal (k_a) and noise (k_w) to make its estimation. We use Beaulieu's approximations here to M-PSK signals and AWGN channels such that k_a=1 and k_w=2. These approximations significantly reduce the complexity of the calculations (and computations) required. Works best for SNR above 1 dB. Reference: D. R. Pauluzzi and N. C. Beaulieu, "A comparison of SNR estimation techniques for the AWGN channel," IEEE Trans. Communications, Vol. 48, No. 10, pp. 1681-1691, 2000.
- SVR: Signal-to-Variation Ratio SNR Estimator. This estimator actually comes from an SNR estimator for M-PSK signals in fading channels, but this implementation is specifically for AWGN channels. The math was simplified to assume a signal and noise kurtosis (k_a and k_w) for M-PSK signals in AWGN. These approximations significantly reduce the complexity of the calculations (and computations) required. Works best for SNR above 0 dB. Original paper: A. L. Brandao, L. B. Lopes, and D. C. McLernon, "In-service monitoring of multipath delay and cochannel interference for indoor mobile communication systems," Proc. IEEE Int. Conf. Communications, vol. 3, pp. 1458-1462, May 1994.
Samples between tags - after this many samples, a tag containing the SNR (key='snr') will be sent on the output port.
Filter Alpha - the update rate of internal running average calculations.