Propagation Path Loss Models for 5G Urban Micro- and Macro-Cellular Scenarios

Sun, Shu; Rappaport, Theodore S.; Rangan, Sundeep; Thomas, Timothy A.; Ghosh, Amitava; Kovács, István Z.; Rodríguez, Inés González; Koymen, Ozge; Partyka, Andrzej; Järveläinen, Jan

doi:10.1109/vtcspring.2016.7504435

Cited by 278 publications

(234 citation statements)

References 18 publications

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“…Results of simulations stated that proposed fractal modeling provides more accurate network analysis in terms of capacity and interference influence in multi-tier heterogeneous networks. The measured path loss values were little bit different compared to the path loss values that stated in [25] as an urban environment. Moreover, Fig.…”

Section: Results and Analysiscontrasting

confidence: 61%

Propagation Measurements and Estimation of Channel Propagation Models in Urban Environment

Zakaria

Ivanek

Glesk³

2017

KSII TIIS

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Wireless communication is a telecommunication technology, which enables wireless transmission between the portable devices to provide wireless access in all types of environments. In this research, the measurements and various empirical models are analysed and compared in order to find out a suitable propagation model to provide guidelines for cell planning of wireless communication systems. The measured data was taken in urban region with low vegetation and some trees at 900 MHz frequency band. Path loss models are useful planning tools, which permit the designers of cellular communication to obtain optimal levels for the base station deployment and meeting the expected service level requirements. Outcomes show that these empirical models tend to overestimate the propagation loss. As one of the key outputs, it was observed that the calculations of Weissberger model fit with the measured data in urban environment.

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Section: Results and Analysiscontrasting

confidence: 61%

Propagation Measurements and Estimation of Channel Propagation Models in Urban Environment

Zakaria

Ivanek

Glesk³

2017

KSII TIIS

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show abstract

“…However, our study is constrained to LAPs only and, therefore, a different approach is adopted here. First, the Close-in (CI) propagation model, i.e., a standard approach for path-loss prediction that uses a d 0 = 1 m close-in free space reference distance, is considered [14]. The CI model depends on two parameters that are the Path Loss Exponent (PLE) and the standard deviation of large scale shadowing σ.…”

Section: Introductionmentioning

confidence: 99%

A study of channel model parameters for aerial base stations at 2.4 GHz in different environments

Sharma

Magarini

Dossi

et al. 2018

2018 15th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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Abstract-The 5th generation of cellular networks (5G) will provide high speed and high-availability wireless links for communication between mobile users. The usage of aerial platforms as base stations has been recently proposed to meet the above requirements, especially in densely-packed urban areas. To make an accurate prediction of the performance in such a communication system the availability of suitable channel models is a fundamental requirement. Here, we concentrate on a simple path loss and shadow fading channel model that is commonly used to describe the propagation between an aerial base station and a user on the ground. A commercial 3D raytracing simulator is used to extract the main parameters used in the model and the Line of Sight/Non Line of Sight probabilities as a function of the transmitter height and elevation angle. We consider three reference scenarios: Suburban, Urban and Urban High Rise generated according to ITU-R specifications. As a novel contribution, we also show simulation results for the spatial correlation of the received signal in the three considered scenarios.

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“…Both LOS and NLOS were modeled as log-distance with lognormal fading, but with different path loss exponents and fading variances as indicated in Table 1: these values are consistent with empirical values measured in [8]. The LOS parameters were used when the straight path from node to sensor ran along streets, while the NLOS values were used for off-street propagation.…”

Section: Channel Assumptionsmentioning

confidence: 79%

“…Following [8] we distinguish between line-of-sight (LOS) and non-line-of-sight (NLOS) propagation. Both LOS and NLOS were modeled as log-distance with lognormal fading, but with different path loss exponents and fading variances as indicated in Table 1: these values are consistent with empirical values measured in [8].…”

Section: Channel Assumptionsmentioning

confidence: 99%

Design and simulation of sensor networks for tracking Wifi users in outdoor urban environments

et al. 2017

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We present a proof-of-concept investigation into the use of sensor networks for tracking of WiFi users in outdoor urban environments. Sensors are fixed, and are capable of measuring signal power from users' WiFi devices. We derive a maximum likelihood estimate for user location based on instantaneous sensor power measurements. The algorithm takes into account the effects of power control, and is self-calibrating in that the signal power model used by the location algorithm is adjusted and improved as part of the operation of the network.Simulation results to verify the system's performance are presented. The simulation scenario is based on a 1.5 km 2 area of lower Manhattan, The self-calibration mechanism was verified for initial rms (root mean square) errors of up to 12 dB in the channel power estimates: rms errors were reduced by over 60% in 300 track-hours, in systems with limited power control. Under typical operating conditions with (without) power control, location rms errors are about 8.5 (5) meters with 90% accuracy within 9 (13) meters, for both pedestrian and vehicular users. The distance error distributions for smaller distances (<30 m) are well-approximated by an exponential distribution, while the distributions for large distance errors have fat tails.The issue of optimal sensor placement in the sensor network is also addressed. We specify a linear programming algorithm for determining sensor placement for networks with reduced number of sensors. In our test case, the algorithm produces a network with 18.5% fewer sensors with comparable accuracy estimation performance.Finally, we discuss future research directions for improving the accuracy and capabilities of sensor network systems in urban environments.

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Propagation Path Loss Models for 5G Urban Micro- and Macro-Cellular Scenarios

Cited by 278 publications

References 18 publications

Propagation Measurements and Estimation of Channel Propagation Models in Urban Environment

Propagation Measurements and Estimation of Channel Propagation Models in Urban Environment

A study of channel model parameters for aerial base stations at 2.4 GHz in different environments

Design and simulation of sensor networks for tracking Wifi users in outdoor urban environments

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