Three Dimensional Measuring Points Locating Algorithm Based Texture-Patched Matrix Completion for Indoor 3D REM Design

• Published in: Wireless personal communications 2022-09, Vol.126 (2), p.1075-1099
• Main Authors: Maiti, Pradipta, Mitra, Debjani
• Format: Article
• Language: English
• Subjects: Algorithms; Cognitive radio; Communications Engineering; Comparative studies; Computer Communication Networks; Correlation coefficients; Engineering; High rise buildings; Networks; Signal,Image and Speech Processing; Texture
• ISSN: 0929-6212; 1572-834X
• DOI: 10.1007/s11277-022-09783-y

Enhanced throughput under efficient dynamic spectrum access is possible by secondary access based cognitive radio networking in TV white space (TVWS). The current generation wireless scenario makes it more applicable to use TVWS indoors within high-rise buildings. The majority of existing literature on TVWS radio environment map (REM) focuses on outdoor scenarios without considering altitude in its design. This paper uses spectrum-sensed real-time data to investigate the feasibility of using the SVT-based matrix completion (MC) technique for constructing indoor 3D REM of an active UHF-TV channel. We have used it to propose a novel strategy of measuring points locating (MPL) algorithm, which, aided by texture-patch transformation (TPT), helps to interpolate 3D REM with a minimum number of measurements. Comparative study of TPT-SVT over other conventional interpolation techniques of IDW, Kriging and KNN has been undertaken on the accuracy, correlation coefficient, time-complexity, and best-fit-line analysis metrics. The 3D REM interpolation over a six-floored building has been executed in two ways—directly by merging floor-by-floor measurements and using TPT for 3D → 2D conversions in conjunction with SVT-MC. Patch choice is derived after thoroughly measuring the symmetric vertical dataset profile. SVT-MC can interpolate accurately but satisfy a minimum number of measurements and locations. MPL can address this problem and select the random measuring points that fit into the TPT-SVT framework. Same interpolation accuracy can also be achieved using fewer such measuring points. At the cost of taking slightly more time, TPT-SVT is, however, more accurate than SVT, IDW, TPT-IDW, KNN, TPT-KNN, Kriging, TPT-Kriging.