An iterative maximum-likelihood-based parameter estimation algorithm for Nakagami-m distribution

Sohail Dianat; Raghuveer Rao

doi:10.1117/12.785123

3 April 2008 An iterative maximum-likelihood-based parameter estimation algorithm for Nakagami-m distribution

Sohail Dianat, Raghuveer Rao

Proceedings Volume 6980, Wireless Sensing and Processing III; 69800P (2008) https://doi.org/10.1117/12.785123
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

Estimation of channel fading parameters is an important task in the design of communication links such as in maximum ratio combining (MRC), where the SNR of the link has to be estimated. The maximum combining weights are directly related to the SNR or the fading channel coefficients. In this paper, we propose iterative techniques based on Maximum Likelihood parameter estimation to estimate the parameters of Nakagami-m distribution in the presence of additive white Gaussian noise. We show that the proposed iterative algorithms converge to a unique solution independent of the initial condition. However, for the purpose of fast convergence, a method is used to find an initial condition close to the true solution. This initial condition is obtained by solving for the unique positive root of a polynomial. Comparisons of our proposed approaches are made with respect to the noise and initial conditions. The performance of the algorithm with respect to the Cramer-Rao bound (CRB) is investigated. Computer simulation results for different signal to noise ratios (SNR) are presented.

Citation Download Citation

Sohail Dianat and Raghuveer Rao "An iterative maximum-likelihood-based parameter estimation algorithm for Nakagami-m distribution", Proc. SPIE 6980, Wireless Sensing and Processing III, 69800P (3 April 2008); https://doi.org/10.1117/12.785123

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