Vehicular LTE connectivity analysis in urban and rural environments using USRP measurements

Vasudeva, Karthik; Özdemir, Özgür; Chandar, Sugan R. S.; Erden, Fatih; Güvenç, İsmail

doi:10.1016/j.array.2020.100045

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…This paper employed the Universal Software Radio Peripheral (USRP) and software-defined radio (SDR) system. Vasudeva et al [ 21 ] used the same hardware platform to analyze measurements for connectivity for vehicles connected over the LTE network in urban and rural locations. They combined the USRP N210 device with LabView as an SDR system and post-processing in MATLAB; however, to produce a cost-effective RNSS, this study used GNU Radio software version 3.8.1.0 [ 22 ] as part of the SDR tool.…”

Section: Literature Reviewmentioning

confidence: 99%

An Assessment of Environmental RF Noise Due to IoT Deployment

Ingala,

Pillay,

Pillay

2023

Sensors

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The advent of the Internet of Things (IoT) has contributed to an increase in the production volume of RF-featured equipment. According to statistics from the literature, the IoT industry will soon deploy billions of products. While the concept behind these applications seems exciting, this paper sought to assess the effects the radio emissions produced by IoT products would have on the ambient radio noise levels within the unlicensed frequency bands of 433 MHz, 868 MHz, and 2.4 GHz. The study extended to three environments: industrial, urban, and suburban. This study developed an IoT noise generator (ING) device to emulate RF noise signals in the desired IoT radio transmission band. The paper presents a simplified radio noise surveying system (RNSS) for data collection of ambient radio noise from five South African candidate sites. The statistical and empirical analysis agree that the level of ambient radio noise was directly proportional to the rate of IoT radio activities. The slopes of the regression lines demonstrate that 80% of the analyzed data developed augmenting trends. Approximately 20% of the data show declining trends.

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Section: Literature Reviewmentioning

confidence: 99%

An Assessment of Environmental RF Noise Due to IoT Deployment

Ingala,

Pillay,

Pillay

2023

Sensors

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“…Dwivedi et al 24) , Sharma et al 25) and Choudhary et al 26) suggested that RSM technology can be used as a multi-objective optimization tool. Karthik Vasudeva et al 27) investigated the V2I connectivity performance of LTE deployments in rural, semi-urban and urban scenarios using extensive drive-test measurement campaign. The joint distributions of the link quality metrics are obtained, which indicate stronger coverage and higher BS density in urban and semi-urban compared to rural scenarios.…”

Section: Related Workmentioning

confidence: 99%

Use of RSM Technology for the Optimization of Received Signal Strength for LTE Signals Under the Influence of Varying Atmospheric Conditions

Choudhary¹,

Sharma²,

Gupta³

et al. 2020

Evergreen

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In the field of telecommunication, network planning plays an important role to maximize the system performance and it is very much related to received signal strength (RSS). While transmitting signal from the transmitter to the receiver, signal has to face so many obstacles like varying environment conditions, high-rise buildings, dense forests etc. Signal strength gets affected with varying distance between the transmitter and receiver too. So for efficient network planning, information about the effect of all these parameters on received signal strength (RSS) should be known to us. So this study aimed to investigate the variation in Received Signal Strength for LTE signal (RSSLTE) with respect to the variation of four input parameters i.e. Relative humidity (25 % to 75 %), Temperature (12 °C to 48 °C), Air quality index for Particulate Matter 2.5 (50 to 500) and Distance from the base station (98 m to 300 m). Optimization of received signal strength was also carried out by using response surface methodology. Optimum value of received Signal Strength for LTE signal (RSSLTE) was found to be -87.0393 dBm when the input parameters i.e.

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