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      RMS Delay Spread vs. Coherence Bandwidth from 5G Indoor Radio Channel Measurements at 3.5 GHz Band

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          Abstract

          Our society has become fully submersed in fourth generation (4G) technologies, setting constant connectivity as the norm. Together with self-driving cars, augmented reality, and upcoming technologies, the new generation of Internet of Things (IoT) devices is pushing the development of fifth generation (5G) communication systems. In 5G architecture, increased capacity, improved data rate, and decreased latency are the objectives. In this paper, a measurement campaign is proposed; we focused on studying the propagation properties of microwaves at a center frequency of 3.5 GHz, commonly used in 5G cellular networks. Wideband measurement data were gathered at various indoor environments with different dimensions and characteristics. A ray-tracing analysis showed that the power spectrum is dominated by the line of sight component together with reflections on two sidewalls, indicating the practical applicability of our results. Two wideband parameters, root mean square delay spread and coherence bandwidth, were estimated for the considered scenarios, and we found that they are highly dependent on the physical dimension of the environment rather than on furniture present in the room. The relationship between both parameters was also investigated to provide support to network planners when obtaining the bandwidth from the delay spread, easily computed by a ray-tracing tool.

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          Most cited references46

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          The indoor radio propagation channel

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            5G: A Tutorial Overview of Standards, Trials, Challenges, Deployment, and Practice

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              Indoor Office Wideband Millimeter-Wave Propagation Measurements and Channel Models at 28 and 73 GHz for Ultra-Dense 5G Wireless Networks

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                29 January 2020
                February 2020
                : 20
                : 3
                : 750
                Affiliations
                [1 ]Department of Signal Theory and Communications-atlanTTic Research Center, University of Vigo, 36310 Vigo, Spain; woutdebaenst@ 123456live.be (W.D.); arne.feys@ 123456outlook.com (A.F.); inhigo@ 123456uvigo.es (I.C.); manuel.garciasanchez@ 123456uvigo.es (M.G.S.)
                [2 ]IDLab, Department of Information Technology, Ghent University-imec, 9052 Gent, Belgium
                Author notes
                Author information
                https://orcid.org/0000-0002-8391-1947
                https://orcid.org/0000-0003-1881-681X
                https://orcid.org/0000-0003-4003-0098
                Article
                sensors-20-00750
                10.3390/s20030750
                7038510
                32013241
                e209139d-e6e6-4896-b19b-c8a8ad451861
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 19 December 2019
                : 27 January 2020
                Categories
                Article

                Biomedical engineering
                indoor propagation,modelling,radio propagation,ray-tracing
                Biomedical engineering
                indoor propagation, modelling, radio propagation, ray-tracing

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