Maximum Entropy Principle: Applicability and Adaptability in Wireless Fading Channels

In pragmatic frameworks, the communication between a transmitter and a receiver is impacted by the simultaneous outcome of wireless fading and shadowing. With regard to probabilistic moments, using the maximum entropy principle (MEP), the characterization of these fading scenarios can be flexibly modeled conditional upon its information set. When Shannon entropy is maximized, the obtained probability distribution identifies a large number of exponential distribution families. Power law distributions, which represent the well-known Shannon family of exponential distributions as q → 1, are produced by maximizing the Tsallis’ entropy with a non-extensive parameter q. To illustrate a broad range of fading conditions, the parameter q can be tuned. Upon maximizing the Shannon entropy, the probability distribution in terms of the Hurwitz zeta function is obtained while taking into account the shifting geometric mean limitations. The equilibrium condition of broadband network traffic is characterized by this density. Furthermore, given specific constraints, the Shannon entropy is maximized in the Laplace domain, resulting in a transient probability distribution that captures the properties of a wide range of fading channels. In this work, the MEP is used to represent the q-Lognormal model. Additionally, the model’s behavior is examined by plotting it against the generated fading signal.