MPSK SNR Estimator

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.


 * 1) 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.
 * 2) 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.
 * 3) 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.
 * 4) 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.

Example Flowgraph
Insert description of flowgraph here, then show a screenshot of the flowgraph and the output if there is an interesting GUI. Currently we have no standard method of uploading the actual flowgraph to the wiki or git repo, unfortunately. The plan is to have an example flowgraph showing how the block might be used, for every block, and the flowgraphs will live in the git repo.

Source Files

 * C++ files
 * TODO


 * Header files
 * TODO


 * Public header files
 * TODO


 * Block definition
 * TODO