Maximum likelihood SNR estimation over time-varying flat-fading SIMO channels

Abstract

In this paper, we propose a new signal-to-noise-ratio (SNR) maximum likelihood (ML) estimator over time-varying single-input multiple-output (SIMO) channels, for both data-aided (DA) and non-data-aided (NDA) cases. Unlike the classical techniques which assume the channel to be slowly time-varying and, therefore, considered as constant during the 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. In the DA scenario, the ML estimator is developed in closed-form expression. In the NDA scenario, however, the ML estimates of the per-antenna SNRs are obtained iteratively, with very few iterations, using the expectation-maximization (EM) procedure. Our estimator is able to accurately estimate the instantaneous SNRs over a wide range of average SNR. We show through extensive Monte-Carlo simulations that the new estimator outperforms previously developed solutions.