Blind signal analysis

Abstract

Blind signal analysis (BSA) plays an essential role in wireless communication when the receiver does not know most or all of the received signal parameters. This chapter provides an in-depth understanding of BSA with laboratory implementation for different applications. The usage of BSA varies depending on the applications and its model. The chapter reviews spectrum sensing, parameter estimation and signal identification, radio environment map, equalization, modulation identification, and multi-carrier parameter estimation in the context of BSA. It presents preliminary information for machine learning (ML) and provides applications of the multidisciplinary domain (BSA and ML) including signal and interference identification, multi-RF impairments identification, channel modeling and estimation, and spectrum prediction with their future directions and challenges. Although the ML paradigm wants to fulfill BSA requirements, there are still some major problems in applying this paradigm practically. The chapter presents a list of these challenges.