Abstract
This paper addresses the research question: can feedforward 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 crowdsourcing with Kriging. In most works on Kriging-aided radio environment mapping, measurement datasets are first 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 in a case where the transmitter is fixed. It is shown that this method does not improve Kriging in a large-scale and fixed 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 ad hoc 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.
Highlights
The growth in the demand for mobile communication systems has exponentially increased data traffic during the last decade
We evaluated the performance where P(x) can be obtained perfectly. This means that path loss modeling was performed without any errors, and the Kriging fully optimizes the interpolation; this value shows a maximum accuracy in this simulation
This paper evaluated the performance of neural network residual Kriging (NNRK) in radio environment mapping
Summary
The growth in the demand for mobile communication systems has exponentially increased data traffic during the last decade. OVERVIEW OF THIS PAPER AND MAIN CONTRIBUTIONS In this study, to answer the above research question, we evaluate the practical performance of FFNN-aided Kriging in radio environment mapping To this end, the path loss is first modeled with an FFNN from actual measured datasets. It is shown that OLS with Kriging achieves a performance almost equal to that of NNRK in a large-scale and fixed transmitter system; the FFNN outperforms OLS in path loss modeling. We demonstrate this using a dataset measured over TV bands This section summarizes ordinary Kriging with OLS-based path loss modeling as a fundamental method in radio environment mapping. The task of Kriging in this context is to interpolate the received signal power at an arbitrary location from the dataset y
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