Intelligent Characterization of Wireless Fading Channels using a Single Statistical q-Weibull distribution

In real-world environment, multipath fading and shadowing have simultaneous effects that are superimposed over the received signal in wireless communication channels. The typical composite models fail to account for the tail fluctuations in the fading channels. In this context, we emphasise the significance and adaptability of the non-extensive parameter q in relation to Tsallis’ entropy when modelling various fading channels. The composite fading channels in this study were described using the q-Weibull distribution. In contrast to the common composite Weibull/Log-normal model, the generated fading signals and the q-Weibull probability density function show excellent agreement. Additionally, a number of performance indicators, including outage probability, channel capacity, and amount of fading, are determined analytically, and the results are validated using Monte-Carlo simulations. Further, considering the complexity of the fading data, machine learning models can provide low complexity solutions towards enhancing the performance of the aforementioned model.