Abstract
Signal-to-noise ratio (SNR) is an important metric for performance assessment in numerous scenerios. In order to ensure the reliability and effectiveness of the system performance, plenty of situations require the information of SNR estimate. At the same time, Mars exploration has been a hot topic in recent years, which leads to the research attention of scholars extending to deep space. In this paper, a new SNR estimator related to deep space scene is proposed. On the one hand, the time of essential data transmission is limited in Mars exploration system. On the other hand, the relative position and condition between orbiters vary quickly all the time, which makes it difficult to obtain the accurate and significant information for Mars exploration. Therefore, it is obvious that the information of SNR can promote the system to adjust the signal transmission rate automatically. Subsequently, the estimation of SNR becomes a fundamental research in automatic digital communications. In this paper, an SNR estimation method based on non-data-aided (NDA) with maximum likelihood (ML) estimation is proposed to enhance the accuracy and reliability of Mars exploration process. Additionally, the Cramer-Rao lower bound (CRLB) related to the designed ML algorithm is derived. Finally, the Monte Carlo simulation results demonstrate that the proposed ML estimator algorithm obtains a superior performance when compared to the existing SNR estimators.
Highlights
Since the Earth and Mars are far away from each other, there is an attenuation of signal and exists a delay of time for communication between them, both of which destroy the signal quality in receivers [1, 2]
The time of essential data transmission is limited in Mars exploration system, and on the other hand, the relative position and condition of orbiters vary quickly all the time, which makes it difficult to obtain the accurate and significant information for Mars exploration [3, 4]
DA estimation algorithms outperform NDA-based methods in accuracy and effectiveness, it is at the expense of lower transmission efficiency, which is extremely worthwhile for deep space exploration [9]
Summary
Since the Earth and Mars are far away from each other, there is an attenuation of signal and exists a delay of time for communication between them, both of which destroy the signal quality in receivers [1, 2]. Sampling points The number of simulation symbols Rolloff coefficient Tap coefficients Signal amplitude The phase of signal The binary source symbols The upsampled information sequence The Kronecker delta The tap coefficients of the RRC filter The signal after being sampled and pulse-shaped The additive white Gaussian noise The variance of AWGN The received signal The output of the MF The downsampled signal of yk The samples of the full raised-cosine The peak of fk The downsampled and filtered samples of noise Signal-to-noise ratio The probability density function The log-likelihood function The estimated parameter vector The decibel scale of θ Cramer-Rao lower bound Fisher information matrix The estimate value of SNR where a represents the signal amplitude whose probability is equal by obtaining values from {−A, A} and αn represents one of the two evenly spaced phases on a unit circle with n ∈ {1, 2, .
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