Radio Environment Map Construction by Residual Kriging Based on
Generalized Regression Neural Network
Abstract
Radio environment map (REM) is an efficient enabler for practical
cognitive radio networks by sensing the electromagnetic information
within regions of interest dynamically. Most of works on Kriging-based
method have proven that separate estimation for pathloss and shadowing
can obtain more accurate REM construction. But these methods have some
shortcomings that prior information is required for construction or
disability for multiple transmitters scenario. In order to overcome the
problems of urban REM construction mentioned above, this paper propose a
residual Kriging algorithm based on generalized regression neural
network (GRNN-RK) for that. The performance of proposed algorithm has
been evaluated by the analysis of simulation results, and experiments
show that GRNN is capable of improving Kriging in accuracy.
Additionally, the influence of spread on REM construction is also
experimented.