Bayesian spatial interpolation as an emerging cognitive radio application for coverage analysis in cellular networks

Abstract

ABSTRACTCoverage prediction is one of the most important aspects of cellular network optimisation for mobile operators due to the highly competitive market conditions and the obligations towards the regulatory authorities. Although wireless communications research is considerably diversifying, including new areas, problems and concepts with a notable pace, cellular coverage still remains as a core subject of research for operators because of the obligation to adapt to the continuously evolving wireless ecosystem, with new radio access technologies/architectures, emerging applications and innovative cellular concepts. This paper presents the application of a powerful mathematical tool coming from spatial statistics, Bayesian kriging, to construct a radio environment map (REM) for the purpose of cellular coverage prediction as an emerging application of cognitive radio techniques in cellular networks. The proposed approach provides an efficient alternative to the conventional manual coverage prediction on the basis of drive tests, which are expensive, polluting and slow solutions for obtaining the ‘ground‐truth’ information. Bayesian kriging‐based REMs allow to estimate the coverage situation in those regions where the operator lacks direct information. Our approach can be directly used by operators for the cellular network coverage optimisation. We evaluate the accuracy of the proposed REM construction approach for a long term evolution network with two mesh sizes using highly realistic data sets. Results show that the Bayesian kriging interpolation technique has a good accuracy for cellular coverage prediction, and this accuracy is directly related with the mesh size. Copyright © 2013 John Wiley & Sons, Ltd.