Spatial Interpolation of Cyclostationary Test Statistics in Cognitive Radio Networks : Methods and Field Measurements

The focus of this paper is on evaluating different spatial interpolation methods for the construction of radio environment map using field measurements obtained by cyclostationary-based mobile sensors. Mobile sensing devices employing cyclostationary detectors provide lot of advantages compared to the widely used energy detectors, such as robustness to noise uncertainty and ability to distinguish among different primary user signals. However, mobile sensing results are not available at locations between the sensors making it difficult for a secondary user (possibly without a spectrum sensor) to decide whether to use primary user resources at that location. To overcome this, spatial interpolation of test statistics measured at limited number of locations can be carried out to create a channel occupancy map at unmeasured locations between the sensors. For this purpose, different spatial interpolation techniques for the cyclostationary test statistic have been employed in this paper such as inverse distance weighting, ordinary kriging, and universal kriging. The effectiveness of these methods is demonstrated by applying them on extensive real-world field measurement data obtained by mobile-phone-compliant spectrum sensors. The field measurements were carried out using four mobile spectrum Manuscript received October 24, 2016; revised March 15, 2017; accepted May 22, 2017. Date of publication June 19, 2017; date of current version February 12, 2018. This work was carried out as a part of the “Enabling Methods for Dynamic Spectrum Access and Cognitive Radio” Project within the TRIAL Technology Programme supported by the TEKES (The Finnish Funding Agency for Technology and Innovation). The work of S. Chaudhari and C. Ramanathan was supported by the Science and Engineering Research Board, India, under Grant ECR/2015/000010. The review of this paper was coordinated by Prof. W. A. Hamouda. (Corresponding author: Sachin Chaudhari.) S. Chaudhari is with the Signal Processing and Communications Research Center, International Institute of Information Technology Hyderabad 500032, India (e-mail: sachin.chaudhari@iiit.ac.in). M. Kosunen, S. Mäkinen, and J. Ryynänen are with the Department of Micro and Nano Sciences, Aalto University School of Electrical Engineering, Espoo 02150, Finland (e-mail: marko.kosunen@aalto.fi; semu.makinen@aalto.fi; jussi.ryynanen@aalto.fi). C. Ramanathan is with the Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036, India (e-mail: ramanathan461@gmail.com). J. Oksanen and V. Koivunen are with the Department of Signal Processing and Acoustics, Aalto University School of Electrical Engineering, Espoo 02150, Finland (e-mail: jan.oksanen@aalto.fi; visa.koivunen@aalto.fi). M. Laatta was with the Tampere University of Technology, Tampere, Finland. He is currently with Wapice Ltd., Tampere, Finland (e-mail: markus.laatta@gmail.com). M. Valkama is with the Department of Electronics and Communications Engineering, Tampere University of Technology, Tampere 33720, Finland (e-mail: mikko.e.valkama@tut.fi). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee. org. Digital Object Identifier 10.1109/TVT.2017. 2717379 sensors measuring eight digital video broadcasting-terrestrial (DVB-T) channels at more than hundred locations encompassing roughly one-third of the area of the city of Espoo in Finland. The accuracy of the spatial interpolation results based on the field measurements is determined using the cross-validation approach with the widely used root mean square error as the metric. Field measurement results indicate that reliable results with spatial coverage can be achieved using kriging for cyclostationary based test statistics. Comparison of spatial interpolation results of cyclostationary test statistics is also carried out with those of energy values obtained during the measurement campaign in the form of received signal strength indicator. Comparison results clearly show the performance improvement and robustness obtained using cyclostationary based detectors instead of energy detectors.