Turning Fiction Into Non-fiction for Signal-to-Noise Ratio Estimation -- The Time-Multiplexed and Adaptive Split-Symbol Moments Estimator
M.K. Simon, Samuel J. Dolinar · Interplanetary Network Progress Report · 2005
A means is proposed for realizing the generalized split-symbol moments estimator (SSME) of signal-to-noise ratio (SNR), i.e., one whose implementation on the average allows for a number of subdivisions (observables), 2L, per symbol beyond the conventional value of two, with other than an integer value of L. In theory, the generalized SSME was previously shown to yield optimum performance for a given true SNR, R, when L = R/ √ 2 and thus, in general, the resulting estimator was referred to as the fictitious SSME. Here we present a time-multiplexed version of the SSME that allows it to achieve its optimum value of L as above (to the extent that it can be computed as the average of a sum of integers) at each value of SNR and as such turns fiction into non-fiction. Also proposed is an adaptive algorithm that allows the SSME to rapidly converge to its optimum value of L when in fact one has no a priori information about the true value of SNR.