About Mohamed A Suliman Mohamed A Suliman Visiting Scholar (former), Information Science Lab Optimal Regularization of Linear System Signal processing machine learning Big data analysis Sensor networks Random Matrix Theory MS Degree at King Abdullah University of Science and Technology, Ph.D. candidate, Imperial College London.. Articles Related News August 2019 SNR Estimation in Linear Systems With Gaussian Matrices 1 min read · Tue, Aug 6 2019 News SNR estimation Guassian matrices ISL Highlighted Publications M. A. Suliman and A. M. Alrashdi and T. Ballal and T. Y. Al-Naffouri, "SNR Estimation in Linear Systems With Gaussian Matrices", IEEE Signal Processing Letters. vol. 24 , pp. 1867-1871, Dec 2017. Abstract: This letter proposes a highly accurate algorithm to estimate the signal-to-noise ratio (SNR) for a linear system from a single realization of the received signal. We assume that the linear system has a Gaussian matrix with one sided left correlation. The unknown entries of the signal and the noise are assumed to be independent and identically distributed with zero mean and can be drawn from
SNR Estimation in Linear Systems With Gaussian Matrices 1 min read · Tue, Aug 6 2019 News SNR estimation Guassian matrices ISL Highlighted Publications M. A. Suliman and A. M. Alrashdi and T. Ballal and T. Y. Al-Naffouri, "SNR Estimation in Linear Systems With Gaussian Matrices", IEEE Signal Processing Letters. vol. 24 , pp. 1867-1871, Dec 2017. Abstract: This letter proposes a highly accurate algorithm to estimate the signal-to-noise ratio (SNR) for a linear system from a single realization of the received signal. We assume that the linear system has a Gaussian matrix with one sided left correlation. The unknown entries of the signal and the noise are assumed to be independent and identically distributed with zero mean and can be drawn from
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