A Novel Indoor Coverage Measurement Scheme Based on FRFT and Gaussian Process Regression

Abstract

Techniques like femtocells are widely used to extend service coverage in indoors and other areas that are unreachable for traditional radio access technologies. In this paper, we consider the indoor coverage measurement problem. We formulate this problem into two parts: the sampling and the reconstruction of received signal strength (RSS) field. Traditional methods cannot solve the RSS fluctuation problem efficiently and require a large number of sensor nodes. To this end, we propose a novel indoor coverage measurement scheme to tackle these problems. First, a fractional Fourier transform (FRFT) based method is proposed to mitigate RSS fluctuation during the sampling process. Then, Gaussian process regression (GPR) is used to reduce the number of sensor nodes being deployed. And a new kernel for GPR is designed to better accomplish the RSS reconstruction task. Simulation analysis shows the proposed scheme can achieve greater advantages in terms of accuracy and flexibility compared with other baselines.