SNR Channel Estimation
Improved SNR estimation techniques for BPSK and QPSK Modulation
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In this book, a technique is proposed in order to estimate the signal to noise ratio (SNR) over a satellite link for fade mitigation techniques (FMT) applications. The channel state of RF link is best provided as SNR estimate of the received signal. And this type of channel state is utilized by satellite capacity optimization techniques like adaptive coding and modulation which comes under the hood of FMT applications. The performance of the SNR algorithm, used as the channel state estimator, directly impacts the efficiency of the FMT. The SNR estimation technique that we implements use non-da...
In this book, a technique is proposed in order to estimate the signal to noise ratio (SNR) over a satellite link for fade mitigation techniques (FMT) applications. The channel state of RF link is best provided as SNR estimate of the received signal. And this type of channel state is utilized by satellite capacity optimization techniques like adaptive coding and modulation which comes under the hood of FMT applications. The performance of the SNR algorithm, used as the channel state estimator, directly impacts the efficiency of the FMT. The SNR estimation technique that we implements use non-data aided technique in which the transmitted symbols are unknown by the receiver, in order to estimate SNR in additive white Gaussian noise (AWGN) channel. Here I analyzed the performance of this technique for both BPSK and QPSK modulation by deriving their normalized mean square error (NMSE) and comparing it with the results of existing algorithms such as M2M4 moments based estimator and ourestimator. Moreover, we discuss how SNR estimates can be used to approximate the NMSE and how the accuracy of the SNR estimate is related to that of the NMSE estimate.
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