Abstract
Radio environment map (REM) can provide important information for designing and optimizing the performance of wireless communication networks. However, the location uncertainty related to the measurements used to build the REM can considerably deteriorate the accuracy of such map. This paper addresses this problem by proposing a modified approach of a classical geostatistical prediction tool, named Kriging method, which incorporates the location uncertainty and is able to improve the REM accuracy without adding significant complexity. Finally, we also show through simulation results that the average path loss and covariance parameter estimation play an important role and should be considered when the location errors occur in the wireless communication systems.
Highlights
R ECENTLY, there has been growing interest in the generation and use of Radio environment map (REM) in wireless communications systems [1,2,3,4,5,6,7]
(iv) The average received power estimation is added to the spatial predictions (i.e., ZKILE, ZKALE and ZS-Kriging adjusted for location error (KALE)) and, the results can be compared with the true values of the spatial random process according to
After formulating the spatial prediction problem, we have shown the necessary steps to generate the REM through the geostatistical based framework
Summary
R ECENTLY, there has been growing interest in the generation and use of Radio environment map (REM) in wireless communications systems [1,2,3,4,5,6,7]. The main contributions of our work can be summarized as follows: We extend our work in [76] by applying a geostatistical model for REM generation and discussing the main peculiarities and limitations of this approach; We incorporate the location uncertainty into the radio environment model and use the methodology in [69,70] to manipulate intractable quantities that arise when location errors are considered in the spatial prediction problem. This allowed us to make adjustments in the conventional Kriging technique to take into account the statistics of the location errors in the spatial predictions, i.e., Kriging adjusted for location error (KALE).
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