A REM-Enabled Diagnostic Framework in Cellular-Based IoT Networks

Chou, Shih-Fan; Yen, Hsiu-Wen; Pang, Ai‐Chun

doi:10.1109/jiot.2019.2900093

Cited by 16 publications

(8 citation statements)

References 35 publications

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“…Lee et al [20] investigated the coverage map constructions by implementing efficient 3-D ray-tracing simulations. Chou et al [21] used the machine learning techniques, including the random forest, the neural network, the linear regression, the decision tree and the gradient boosting, to construct the radio environment map.…”

Section: Related Workmentioning

confidence: 99%

“…Numerical model methods can estimate the KPIs of all sampling points which are intensive and even inaccessible for vehicle in DT methods by numerical calculations, such as model calculations [18]- [21] and interpolation methods [22]- [27], to construct more refined coverage maps. However, the computation speeds of such algorithms are slow due to the large number of the sampling points.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Criteria Coverage Map Construction Based on Adaptive Triangulation-Induced Interpolation for Cellular Networks

et al. 2019

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Wireless cellular communications lead to huge demands for estimating and visualizing the data about Quality of Service (QoS) for mobile network operators in the 5th Generation (5G) networks. Constructing a coverage map is an important step to visualize global information about QoS. Inspired by the characteristic of the base station, we present an adaptive triangulation method to divide the region of interest into triangles. Then, we propose a novel area-wise Multi-criteria Triangulation-induced Interpolation (MTI) algorithm which utilizes the linear interpolation to estimate the key performance indicators of the QoS inside a triangle with the known values of its three vertexes, to construct the coverage maps and provide the closedform solution of the covered region for the multi-criteria problems. We check the accuracy and the efficiency of the MTI algorithm both in 19-cells network scenario and in real big city scenario. The experiment results manifest that the MTI algorithm shows a good performance in constructing the coverage maps and it is significantly lower-cost and higher-efficiency than the traditional point-wise algorithms. INDEX TERMS Wireless cellular communication, coverage map, triangulation method, linear interpolation.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Coverage Map Construction Based on Adaptive Triangulation-Induced Interpolation for Cellular Networks

et al. 2019

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“…In [36], some experimental studies were done in indoor same/multi floor environment, validating a previous simulation model and detecting availability of TV-WS and TV-GS in indoor. The experiments are mainly aimed into the detection accuracy of TV-WS area in 2D indoor space for estimating transmitting power of D2D communication using TV-WS in [32]. In [37], 2D outdoor REM has been generated on randomly deployed sensors data using kriging, natural neighbor and IDW interpolation methods to identify white spaces inside and in [38], interpolation has done using IDW and Kriging for 2D REM generation.…”

Section: It Has Been Observed Inmentioning

confidence: 99%

“…It has been reported in [31] that combined WSs and GSs ratio in 3D indoor is much higher than that from the 2D outdoor. Altitude consideration using FFA explores more TV-GS volume accurately than 2D area for cognitive operations and other short distance applications [18], [20], [27]- [32].…”

Section: Introductionmentioning

confidence: 99%

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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“…Currently, a significant variety of contemporary and upcoming use‐cases are being catered to by REMs for example, 5G and IoT network optimization, 12,13 wireless resource allocation, 14 cooperative communication, 15 transmitter localization, 16 UAV based access point placement, 17 network diagnostics, 18 Multi‐hop routing, 19 and military applications 20 . In order to effectively empower all of the above mentioned and more applications, it is imperative that the REM creation process is quick.…”

Section: Introductionmentioning

confidence: 99%

Gaussian mixture model based radio environment mapping for an underlay Device‐to‐Device network

Sengar

Gangopadhyay

Debnath

2023

Trans Emerging Tel Tech

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A REM-Enabled Diagnostic Framework in Cellular-Based IoT Networks

Cited by 16 publications

References 35 publications

Multi-Criteria Coverage Map Construction Based on Adaptive Triangulation-Induced Interpolation for Cellular Networks

Multi-Criteria Coverage Map Construction Based on Adaptive Triangulation-Induced Interpolation for Cellular Networks

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

Gaussian mixture model based radio environment mapping for an underlay Device‐to‐Device network

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