Performance Evaluation of Wireless Communication Systems over Weibull/q-Lognormal Shadowed Fading Using Tsallis’ Entropy Framework

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

“…In Tsallis entropy framework, the value of the non-extensive parameter q between the proposed q-Lognormal distribution and synthesized fading data is estimated by optimizing the generic Jensen-Shannon (JS) divergence. In contrary to the commonly modeled lognormal distribution, as q > 1 the proposed model portrays the long-tailed characteristics that provides an excellent agreement over the outliers in the fading signals [23].…”

“…The amplitude fluctuations as a result of shadowing is often modeled using the log-normal distribution with the fluctuation parameter satisfying the log distance path loss model [2,3,15]. The log-normal distribution is often averaged with several other distributions viz., the Rayleigh distribution, the Weibull distribution to contemplate the concurrent effects of fading and shadowing [23,18,27]. It is observed that the log-normal distribution has an ascendancy over other slow fading channel models viz., gamma distribution, inverse gaussian distribution [4,16] in capturing the long range fading signals.…”

“…It is observed that the log-normal distribution has an ascendancy over other slow fading channel models viz., gamma distribution, inverse gaussian distribution [4,16] in capturing the long range fading signals. However, the widely used conventional log-normal model fails to capture the extreme fluctuations due to its inability in distinguishing the tail behavior or outliers of the fading signals [23,27]. The outliers are the regions in the signal where the extreme fluctuations are observed due to various obstructions between the transmitter and the receiver, and this should be analyzed effectively for error free signal transmission and evaluation of performance measures.…”

“…In this context, a framework incorporating maximum entropy principle based on non-extensive parameter 'q' defined by Tsallis' is portrayed to explain the extreme tail fluctuations of the fading signals [10,9,14,28,7]. The specification of the normalization constraint along with first and second moment constraints results in the q-Lognormal model [23] for the continuous range of parameter 'q', 1 < q < 3. The presented q-Lognormal model can characterize an entire scale of tail variations in the real valued range of non-extensive parameter 'q', and the model adapts to different fading scenarios.…”

An adaptive q-Lognormal model towards the computation of Average Channel Capacity in slow fading channels

“…It is well-known that the lognormal distribution characterizes long term fading or shadowing effects [1] . However, the proposed model well characterizes the tail variations over the synthesized fading signals [23]. From Fig.…”