A Channel Classification Scheme Accounting for Nakagami-m Shadowing and FTR Model

Abstract

In this letter, we address the problem of channel classification. Specifically, we consider five channel models also accounting for the incoming fifth generation mobile communication network (5G). The classification problem is formulated in terms of a multiple hypothesis testing problem and solved through a heuristic decision logic relying on sequential binary comparisons. This choice is dictated by the mathematical intractability of probability density functions (PDFs) under some hypotheses which contain transcendental functions. As a consequence, decision rules raising from well-known design criteria based upon data distribution might suffer numerical instability related to the routines used to implement such PDFs. The performance analysis is conducted on simulated data and shows the effectiveness of the proposed strategy in channel classification.