We consider the problem of estimating the frame error rate (FER) of a given memoryless binary symmetric channel by observing the success or failure of transmitted packets. Whereas FER estimation is relatively straightforward if all observations correspond to packets with equal length, the problem becomes considerably more complex when this is not the case. We develop FER estimators when

transmissions of different lengths are observed, together with the Cramer-Rao Lower Bound (CRLB). Although the main focus is on Maximum Likelihood (ML) estimation, we also obtain low complexity schemes performing close to optimal in some scenarios. In a second stage, we consider the case in which FER estimation is performed at a node different from the receiver, and incorporate the impairment of unreliable observations by considering noisy ACK/NAK feedback links. The impact of unreliable feedback is analyzed by means of the corresponding CRLB. In this setting, the ML estimator is obtained by applying the Expectation-Maximization algorithm to jointly estimate the error probabilities of the data and feedback links. Simulation results illustrate the benefits of the proposed estimators.

%B IEEE Transactions on Wireless Communications
%V 16
%P 3661 - 3673
%8 06/2017
%G eng
%N 6
%R 10.1109/TWC.2017.2686845
%0 Journal Article
%J IEEE Trans. Signal Processing
%D 2015
%T Cramér-Rao Bounds for SNR Estimation of Oversampled Linearly Modulated Signals
%A R. López-Valcarce
%A J. Villares
%A J. Riba
%A W. Gappmair
%A C. Mosquera
%K adaptive signal processing
%K compass
%K Cramér-Rao bound (CRB)
%K dynacs
%K oversampling
%K Signal-to-noise ratio (SNR)
%X Most Signal-to-noise ratio (SNR) estimators use the

receiver matched filter output sampled at the symbol rate, an

approach which does not preserve all information in the analog

waveform due to aliasing. Thus, it is relevant to ask whether

avoiding aliasing could improve SNR estimation. To this end, we

compute the corresponding data-aided (DA) and non-data-aided

(NDA) Cramér-Rao bounds (CRBs). We adopt a novel dual filter

framework, which is shown to be information-preserving under

suitable conditions and considerably simplifies the analysis. It is

shown that the CRB can be substantially reduced by exploiting

any available excess bandwidth, depending on the modulation

scheme, the SNR range, and the estimator (DA or NDA) type.

%B IEEE Trans. Signal Processing
%V 63
%P 1675-1683
%8 04/2015
%G eng
%N 7
%& 1675
%R 10.1109/TSP.2015.2396013