Abstract
In this paper, signal-to-noise ratio (SNR) estimation is carried out by the method of moments (MOM) for fading channels modeled by probability distributions η−μ and κ−μ, considering M-ary quadrature amplitude modulation (M-QAM) with constellation energy normalized to one. New expressions are presented for the SNR estimation and for the mean, variance, and normalized mean square error (NMSE) of the estimates, obtained by a statistical linearization argument. Additionally, it is shown how to obtain the SNR estimate for Nakagami-m channel from the estimation derived for the models η−μ and κ−μ. The results obtained from the analytical expressions are corroborated by simulation results and show that the MOM is a suitable alternative for scenarios in which the mathematical tractability does not suggest the application of other estimation techniques.
Highlights
Signal-to-noise ratio (SNR) estimation has been a recurrent research topic, due to the relevance of SNR for a variety of mobile communication systems
In [5], SNR is a useful parameter in the scenario of turbo decoding systems, while in [6] it is useful in the context of low-density parity check (LDPC) codes
9 Results and discussion results are presented for numerical evaluation of the mathematical expressions obtained for the estimates of the SNR and its normalized mean square error (NMSE), corroborated by simulations performed by the Monte Carlo method
Summary
Signal-to-noise ratio (SNR) estimation has been a recurrent research topic, due to the relevance of SNR for a variety of mobile communication systems. The a priori knowledge of the communication channel conditions is an important issue as long as those systems become more complex and widely required. In [1] for instance, the a priori knowledge of the channel, by means of the SNR, is proposed for evaluating the effective transmission rate (throughput) in a communication system with adaptive modulation and coding, while in [2] its use is considered in adaptive transmission systems. In [3], the knowledge of the SNR is necessary for assessing the time-varying channel condition of an adaptive system with frequency hopping, and in [4] it is necessary for planning relay communication systems. One can use a method belonging to the class of estimators that use a training sequence (that is, a data-aided method (DA)) [8, 9], or a method belonging to the class of estimators that do not have a priori knowledge of the transmitted sequence of symbols (that is, a non-data-aided method (NDA)) [10]
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