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Prior asymptotic performance analyses are based on the series expansion ofthe moment-generating function (MGF) or the probability density function (PDF)of channel coefficients. However, these techniques fail for lognormal fadingchannels because the Taylor series of the PDF of a lognormal random variable iszero at the origin and the MGF does not have an explicit form. Althoughlognormal fading model has been widely applied in wireless communications andfree-space optical communications, few analytical tools are available toprovide elegant performance expressions for correlated lognormal channels. Inthis work, we propose a novel framework to analyze the asymptotic outageprobabilities of selection combining (SC), equal-gain combining (EGC) andmaximum-ratio combining (MRC) over equally correlated lognormal fadingchannels. Based on these closed-form results, we reveal the followings: i) theoutage probability of EGC or MRC becomes an infinitely small quantity comparedto that of SC at large signal-to-noise ratio (SNR); ii) channel correlation canresult in an infinite performance loss at large SNR. More importantly, theanalyses reveal insights into the long-standing problem of performance analysesover correlated lognormal channels at high SNR, and circumvent thetime-consuming Monte Carlo simulation and numerical integration.

A New Asymptotic Analysis Technique for Diversity Receptions Over Correlated Lognormal Fading Channels