Building accurate radio environment maps from multi-fidelity spectrum sensing data

Abstract

In cognitive wireless networks, active monitoring of the wireless environment is often performed through advanced spectrum sensing and network sniffing. This leads to a set of spatially distributed measurements which are collected from different sensing devices. Nowadays, several interpolation methods (e.g. Kriging) are available and can be used to combine these measurements into a single globally accurate radio environment map that covers a certain geographical area. However, the calibration of multi-fidelity measurements from heterogeneous sensing devices, and the integration into a map is a challenging problem. In this paper, the auto-regressive co-Kriging model is proposed as a novel solution. The algorithm is applied to model measurements which are collected in a heterogeneous wireless testbed environment, and the effectiveness of the new methodology is validated.

Parameter Estimation for the Field Strength of Radio Environment Maps

“…In this paper, the proposed algorithm was compared with three kinds of mainstream algorithms, which are IDW [14-17, 20, 21, 24], Spline [16,20,25], and Kriging [11][12][13][14][15][16][17][18][19][20][21]. We tested them on two kinds of data sets and analyzed the performance of the algorithms through a variety of objective evaluation indexes.…”

“…The Kriging method is a method based on the spatial analysis of a variogram, which is an unbiased optimal estimation of regionalized variables over a finite area, and is considered to be the best method for estimating the parameters of the radio environment map [11][12][13][13][14][15][16][17][18][19][20][21]. The Kriging method is divided into ordinary Kriging and universal Kriging depending on the existence of space field drift.…”

“…For the fitness function defined by (14), the PSO algorithm is used to fit the weighted variogram. In the particle swarm, the position of the ith particle can be expressed as = ( 1 , 2 , .…”

“…(2) Evaluation of particles: the fitness value of each particle is calculated using Equation (14). (3) Updating best : the particle fitness values are compared with the population optimal value best , and if the current value is better than best , the position of best is set to the current particle position.…”

“…In this paper, we call it radio environment map parameter estimation. This problem, especially since 2012, has been increasingly studied in depth [11][12][13][14][15].…”