Maximum likelihood SNR estimation ofudlinearly-modulated signals over time-varyingudflat-fading SIMO channels using theudexpectation-maximization concept.

摘要

In this thesis, we tackle the problem of maximum likelihood (ML) estimation of the signal-to-noise ratio (SNR) parameter over time-varying single-input multiple-output (SIMO) channels, for both data-aided (DA) and non-data-aided (NDA) scenarios. Unlike classical techniques where the channel is assumed to be slowly time-varying and therefore considered as constant over the entire observation period, we address the more challenging problem of instantaneous SNR estimation over fast time-varying channels. The channel variations are locally tracked using a polynomial-in-time expansion. First, we derive in closed-form expressions the DA ML estimator along with its bias. The latter is subsequently subtracted in order to obtain a new unbiased estimator whose variance and the corresponding Cramér-Rao lower bound (CRLB) are also derived in closed-form. Due to the extreme nonlinearity of the log-likelihood fonction in the NDA case, we resort to the expectation-maximization (EM) technique to iteratively obtain the exact NDA ML SNR estimates within very few iterations. The new estimators are able to accurately estimate the instantaneous per-antenna SNRs over a wide practical SNR range. In particular, the new NDA ML estimator exhibits a substantial performance advantage against the WG L technique [4], the only suitable benchmark available in the literature so far on SNR estimation over time-varying channels, not only in its original single-input single-output (SISO) version but also against its SIMO extension that is derived and detailed later in this thesis.