Time-Spectrum-Space Three Dimensions Radio Environment Map Construction and Utilization in TV White Space

Zhang, Qixun; Liu, Shang; Huang, Yajian; Zhang, Feng

doi:10.1007/s11277-015-3009-7

Cited by 5 publications

(2 citation statements)

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“…The author Zhang, Q, Liu, S., Huang, Y. has proposed a novel time-spectrum space 3D-REM in their paper [33]. They have done field measurements for outdoor as well as indoor measurements to detect TV-WS and TV-GS area accurately.…”

Section: Literature Study Of Related Workmentioning

confidence: 99%

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

Maiti

Mitra

2021

Preprint

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In this paper, a novel texture-patch transformed (TPT) three dimensional (3D) matrix completion (MC) method has been proposed with the support of novel 3D measuring points (MPs) locating algorithm to generate practical received signal strength (RSS) database assisted indoor 3D radio environment map (REM) of ultra-high frequency (UHF) television (TV)-band. The exploration of TV-band results in TV white and grey space (TV-WS and TV-GS), which are competent resolution to recoup excess data traffic through cognitive radio networks (CRNs) by dynamic spectrum access (DSA) by secondary user (SU). Maximum wireless data traffic generates in indoor and altitude considered exploration of REM achieves high data rate, so selecting interpolation algorithm is important for getting accurate and timely generated REM. Many MC algorithm shows better results than standard interpolation methods. Instead of using layer-by-layer MC algorithm, TPT-MC algorithm could be used through 3D↔2D conversion. Patch size has been considered through symmetric dataset profile. MC criteria based analysis shows TPT-MC algorithm takes lesser no. of MPs than layer-by-layer MC algorithm. Singular value thresholding (SVT) algorithm is used MC algorithm. TPT-SVT shows advantage over layer-by-layer SVT algorithm on RMSE, correlation, best-fit-line and simulation time on same no. of dataset. The result analysis shows that TPT-SVT algorithm is better in RMSE, closest best-fit-line and correlation coefficient than 2D IDW2, 2D K-NN, 2D kriging, TPT-IDW2, TPT-K-NN, TPT-kriging, 3D IDW2 and layer-by-layer SVT algorithm. Computation time of TPT-SVT is better than 3D IDW2 and SVT. TPT-SVT algorithm takes lesser no. of dataset than SVT algorithm for faithful MC.

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Section: Literature Study Of Related Workmentioning

confidence: 99%

“…In previous works, it is observed that no indoor 3D REM explored using interpolation methods considering altitude and different indoor losses [28]- [30] [33]. Whole spectrum data collected by field measurements considering receiver antenna height (RAH) and there is no exploration of indoor 3D REM by using 3D interpolation [27] [20], [31].…”

Section: Introductionmentioning

confidence: 99%