Signal stripping based sensing parameter estimation in perceptive mobile networks

Abstract

We propose signal stripping based approaches for estimating detailed channel composition parameters for sensing in the recently proposed perceptive mobile networks where simultaneous communication and radar sensing is performed. Via reconstructing a (differentiated) channel matrix, we obtain a signal model which can be solved by conventional compressive sensing (CS) techniques. Parameter estimation methods based on a 1-D CS algorithm are proposed and their effectiveness is validated by provided simulation results.