Calibration of ray-tracing simulator for millimeter-wave outdoor communications

The high-speed railway (HSR) wireless channel models based on field measurements have poor universality and low modeling accuracy due to the limitations of the experimental methods and the terrain conditions. To overcome this problem, this paper considers the wireless channels in various HSR scenarios (such as tunnels, mountains, viaducts, cuttings and plains) as the research objects and establishes a novel finite-state Markov chain (FSMC) optimization simulation model based on the signal-to-noise ratio (SNR) threshold, the channel states and the state transition probability matrix, by using the nonuniform space division SNR quantization strategy (hereinafter referred to as Strategy 1) and the equal-area space division SNR quantization strategy (hereinafter referred to as Strategy 2). The SNR curves that are obtained via simulation closely fit the experimental results; therefore, the proposed simulation model can accurately characterize the channel state in a variety of HSR scenarios. Furthermore, the simulation results demonstrate that in the tunnel scenario, Strategy 1 realizes a smaller mean square error (MSE) and a higher modeling accuracy than Strategy 2. The MSE values of the two strategies are similar in the plain scenario. Strategy 2 realizes a smaller MSE and a higher modeling accuracy in the mountain, viaduct and cutting scenarios.

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“…According to (7) and (8), the reference distance of the each dividing points in a space can be obtained, namely,…”

Section: ) Non-uniform Space Divisionmentioning

confidence: 99%

“…Different channel models are established according to different modeling methods. In [7]- [9], a deterministic channel model of ray tracing algorithm is proposed, which is commonly used in tunnels, viaducts and other environments. In [10], [11], the geometric random model is used, which is a semi-deterministic modeling method.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of Wireless Channel in High-Speed Railway Terrain

Xie

Zhang

et al. 2020

IEEE Access

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“…Similarly, in [104], the radio-planning analysis of a Wi-Fi network in a university campus is presented using an RT approach. In addition, it has also been reported in the literature the calibration of RT simulators for millimeter-wave propagation analyses based on the measured results in a university campus at 28 GHz [105] and 38 GHz [106]. However, all the presented works are focused on micro-cellular environments and on analyzing the wireless propagation channel between a base station at a certain height in a building and a client in a pedestrian street.…”

Section: Related Workmentioning

confidence: 99%

Design and Experimental Validation of a LoRaWAN Fog Computing Based Architecture for IoT Enabled Smart Campus Applications

Fraga‐Lamas

Celaya-Echarri

López-Iturri

et al. 2019

Sensors

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A smart campus is an intelligent infrastructure where smart sensors and actuators collaborate to collect information and interact with the machines, tools, and users of a university campus. As in a smart city, a smart campus represents a challenging scenario for Internet of Things (IoT) networks, especially in terms of cost, coverage, availability, latency, power consumption, and scalability. The technologies employed so far to cope with such a scenario are not yet able to manage simultaneously all the previously mentioned demanding requirements. Nevertheless, recent paradigms such as fog computing, which extends cloud computing to the edge of a network, make possible low-latency and location-aware IoT applications. Moreover, technologies such as Low-Power Wide-Area Networks (LPWANs) have emerged as a promising solution to provide low-cost and low-power consumption connectivity to nodes spread throughout a wide area. Specifically, the Long-Range Wide-Area Network (LoRaWAN) standard is one of the most recent developments, receiving attention both from industry and academia. In this article, the use of a LoRaWAN fog computing-based architecture is proposed for providing connectivity to IoT nodes deployed in a campus of the University of A Coruña (UDC), Spain. To validate the proposed system, the smart campus has been recreated realistically through an in-house developed 3D Ray-Launching radio-planning simulator that is able to take into consideration even small details, such as traffic lights, vehicles, people, buildings, urban furniture, or vegetation. The developed tool can provide accurate radio propagation estimations within the smart campus scenario in terms of coverage, capacity, and energy efficiency of the network. The results obtained with the planning simulator can then be compared with empirical measurements to assess the operating conditions and the system accuracy. Specifically, this article presents experiments that show the accurate results obtained by the planning simulator in the largest scenario ever built for it (a campus that covers an area of 26,000 m 2 ), which are corroborated with empirical measurements. Then, how the tool can be used to design the deployment of LoRaWAN infrastructure for three smart campus outdoor applications is explained: a mobility pattern detection system, a smart irrigation solution, and a smart traffic-monitoring deployment. Consequently, the presented results provide guidelines to smart campus designers and developers, and for easing LoRaWAN network deployment and research in other smart campuses and large environments such as smart cities.

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Interference identification in smart grid communications

Yin

Miao

et al. 2018

World Wide Web

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Calibration of ray-tracing simulator for millimeter-wave outdoor communications

Cited by 5 publications

References 7 publications

Modeling and Optimization of Wireless Channel in High-Speed Railway Terrain

Modeling and Optimization of Wireless Channel in High-Speed Railway Terrain

Design and Experimental Validation of a LoRaWAN Fog Computing Based Architecture for IoT Enabled Smart Campus Applications

Interference identification in smart grid communications

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