A Ray Launching-Neural Network Approach for Radio Wave Propagation Analysis in Complex Indoor Environments

Azpilicueta, Leyre; Rawat, Meenakshi; Rawat, Karun; Ghannouchi, Fadhel M.; Falcone, Francisco

doi:10.1109/tap.2014.2308518

Cited by 128 publications

(107 citation statements)

References 32 publications

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“…Algorithms Applications [44] RBF-NN Predict the path loss [45] RVM Estimate DoA of MPCs [46] RVM Filter the noise embedded MPCs to determine the PDP [47] FNN and RBF-NN Predict the intermediate points in ray launching simulation to obtain the indoor received power [48] ANN Produce the CIR with a limited number of cluster-nuclei [49], [50] ANN and PCA Remove the noise and estimate the CIR [51] MLP Predict the outdoor received signal strength [52] CNN Extract channel features and identify different wireless channels based on channel measurement data [53] LASSO Estimate the CIR based on channel measurement data [54] SVM Predict the path loss [55] K-means, FCM, and DBSCAN Clustering and tracking MPCs MLP was applied to predict the received signal strength.…”

Section: Refmentioning

confidence: 99%

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A Big Data Enabled Channel Model for 5G Wireless Communication Systems

Huang

Wang

Bai

et al. 2020

IEEE Trans. Big Data

100

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The standardization process of the fifth generation (5G) wireless communications has recently been accelerated and the first commercial 5G services would be provided as early as in 2018. The increasing of enormous smartphones, new complex scenarios, large frequency bands, massive antenna elements, and dense small cells will generate big datasets and bring 5G communications to the era of big data. This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling. We propose a big data and machine learning enabled wireless channel model framework. The proposed channel model is based on artificial neural networks (ANNs), including feed-forward neural network (FNN) and radial basis function neural network (RBF-NN). The input parameters are transmitter (Tx) and receiver (Rx) coordinates, Tx-Rx distance, and carrier frequency, while the output parameters are channel statistical properties, including the received power, root mean square (RMS) delay spread (DS), and RMS angle spreads (ASs). Datasets used to train and test the ANNs are collected from both real channel measurements and a geometry based stochastic model (GBSM). Simulation results show good performance and indicate that machine learning algorithms can be powerful analytical tools for future measurement-based wireless channel modeling.

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Section: Refmentioning

confidence: 99%

“…RBF-NN uses a single layer with a very high number of neurons with the same goal of achieving higher number of non-linear segmentation. Because neurons are added as learning continues and only one layer of weights is to be adjusted for RBF-NN, its computational cost of learning is less than that for FNN [47].…”

Section: Ann Based Channel Modelmentioning

confidence: 99%

A Big Data Enabled Channel Model for 5G Wireless Communication Systems

Huang

Wang

Bai

et al. 2020

IEEE Trans. Big Data

100

View full text Add to dashboard Cite

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“…In order to analyze wireless system performance, a conventional indoor scenario has been implemented, depicted in Figure 1. In order to perform the wireless analysis, an in-house implemented 3D Ray Launching code has been employed, based on Geometric Optics and Uniform Theory of Diffraction and enhanced with acceleration functions based on Neural Network interpolators, Collaborative Filtering database extraction and Electromagnetic Diffusion Equation hybrid simulation, among other [3][4]. The employed simulation parameters are defined in Table 1.…”

Section: Scenario Description and Wireless System Analysismentioning

confidence: 99%

Wireless System Integration to Enable Smart Cities and Smart Regions

Azpilicueta

López-Iturri

Aguirre

et al. 2017

The 4th International Electronic Conference on Sensors and Applications

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Abstract:The advent of Smart Cities and its extension to Smart Regions requires seamless interaction of systems as well as with users, in a context where a great deal of devices exhibit potential network connectivity. Wireless systems are key elements in order to enable high interactivity, with multiple different systems operating simultaneously within a given region. Multiple network coordination and analysis is compulsory in order to enhance coverage/capcity relations, whilst achieving required bit rates and Quality of Service demands. In this work, the analysis of multiple wireless systems, based on the combination of WLAN/WBAN/NFC will be analyzed in the context of Smart Cities, examining inter-operation performance and overall deployment considerations.

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“…When the ray impacts with an obstacle, reflection, transmission and diffraction will occur, depending on the geometry and the electric properties of the object, as is depicted in Figure 2. A deterministic method based on an in-house developed 3-D Ray Launching code has been used to analyze the radio electric behavior of an inhomogeneous vegetation environment [25,26]. The software has been implemented in-house based on Matlab programming environment.…”

Section: ┴║mentioning

confidence: 99%

Analysis of Radio Wave Propagation for ISM 2.4GHz Wireless Sensor Networks in Inhomogeneous Vegetation Environments

Azpilicueta

Lopez

Aguirre

et al. 2014

Proceedings of International Electronic Conference on Sensors and Applications

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Abstract:The use of wireless networks has experienced exponential growth due to the improvements in terms of battery life and low consumption of the devices. However, it is compulsory to conduct previous radio propagation analysis when deploying a wireless sensor network. These studies are necessary to perform an estimation of the range coverage, in order to optimize the distance between devices in an actual network deployment. In this work, the radio channel characterization for ISM 2.4 GHz Wireless Sensor Networks (WSNs) in an inhomogeneous vegetation environment has been analyzed. This analysis allows designing environment monitoring tools based on ZigBee and WiFi where WSN and smartphones cooperate, providing rich and customized monitoring information to users in a friendly manner. The impact of topology as well as morphology of the environment is assessed by means of an in-house developed 3D Ray Launching code, to emulate the realistic operation in the framework of the scenario. Experimental results gathered from a measurement campaign conducted by deploying a ZigBee Wireless Sensor Network, are analyzed and compared with simulations in this paper. The scenario where this network is intended to operate is a combination of buildings and diverse vegetation species. To gain insight in the effects of radio propagation, a OPEN ACCESSSensors 2014, 14 23651 simplified vegetation model has been developed, considering the material parameters and simplified geometry embedded in the simulation scenario. An initial location-based application has been implemented in a real scenario, to test the functionality within a context aware scenario. The use of deterministic tools can aid to know the impact of the topological influence in the deployment of the optimal Wireless Sensor Network in terms of capacity, coverage and energy consumption, making the use of these systems attractive for multiple applications in inhomogeneous vegetation environments.

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A Ray Launching-Neural Network Approach for Radio Wave Propagation Analysis in Complex Indoor Environments

Cited by 128 publications

References 32 publications

A Big Data Enabled Channel Model for 5G Wireless Communication Systems

A Big Data Enabled Channel Model for 5G Wireless Communication Systems

Wireless System Integration to Enable Smart Cities and Smart Regions

Analysis of Radio Wave Propagation for ISM 2.4GHz Wireless Sensor Networks in Inhomogeneous Vegetation Environments

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