An adaptive q-Lognormal model towards the computation of average channel capacity in slow fading channels

The characterization of multipath fading and shadowing in wireless communication systems is essential towards the evaluation various performance measures. It well known that statistical phenomena captured by distributions viz., log-normal distribution, gamma distribution other mixture distributions. However, it observed fails to characterize outliers signal. extreme fluctuations signal needs be characterized efficiently for error free computation metrics. In this context, paper portrays an adaptive generalized Tsallis’ non-extensive q-Lognormal model channels. This operates with synthesized signals captures wide range tail adapt different scenarios. significance applicability proposed novel q-lognormal capturing slow channels validated using tests chi-square test symmetric JS measure. Furthermore, measures average channel capacity, closed form expression cumulative function (CDF) terms Gauss-Hypergeometric $${}_2{F_1}\left[ {\mathrm{{a, b, c; z}}} \right] $$ , moment generating function, higher order moments corresponding capacity coefficient variation evaluated performing extensive Monte-Carlo simulation techniques up $$O(10^7)$$ .