IEEE Project Abstract

This paper addresses the research question: can feed forward neural network (FFNN)-based path loss modeling improve the accuracy of Kriging? Radio propagation factors, which consist of path loss and shadowing, can accurately be obtained via crowd sourcing with Kriging. In most works on Kriging-aidedradio environment mapping, measurement data sets are rst regressed via linear path loss modeling to ensure spatial stationarity of the shadowing. However, in practical situations, the path loss often contains an anisotropy owing to terrain and obstacle effects. Thus, Kriging may not perform an optimal interpolation because of the errors in path loss modeling. In this paper, an FFNN is used for path loss modeling. Then,ordinary Kriging is applied to interpolate the shadowing.We first evaluate the performance of this method ina case where the transmitter is fixed. It is shown that this method does not improve Kriging in a large-scaleand xed transmitter system; although the FFNN outperforms OLS in path loss modeling. Then, this method is extended to distributed wireless networks where transmitters are arbitrarily located, such as in mobile adhoc networks (MANETs) and vehicular ad hoc networks (VANETs). The results of a measurement-based experiment show that the FFNN is capable of improving Kriging in such a distributed network case.