Characterisation of highly absorbent and highly reflective radio wave propagation environments in industrial applications

Ferrer-Coll, Javier; Ängskog, Per; Chilo, José; Stenumgaard, Peter

doi:10.1049/iet-com.2012.0028

Cited by 25 publications

(21 citation statements)

References 15 publications

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“…The measurements were conducted along two measurement routes using two access points installed at 2 m and 4.85 m with the receiver being at 1 m above the ground. Measurements spanning over a large frequency band 200-2500 MHz were also carried out in an industrial environment [7] where the transmitter/receiver antennas were mounted 1 m above the floor. The measurements included 1601 measurement points with a maximum distance of 18 m between the transmitter and receiver locations.…”

Section: Introductionmentioning

confidence: 99%

Radio Propagation Analysis of Industrial Scenarios within the Context of Ultra-Reliable Communication

Wassie

Rodríguez

Berardinelli

et al. 2018

2018 IEEE 87th Vehicular Technology Conference (VTC Spring)

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One of the 5G use cases, known as ultra-reliable communication (URC), is expected to support very low packet error rate on the order of 10 −5 with a 1 ms latency. In an industrial scenario, this would make possible replacing wired connections with wireless for controlling critical processes. Industrial environments with large metallic machinery and concrete structures can lead to deep shadowing and severe fading in the radio propagation channel, and thus pose a challenge for achieving the outage levels in connection with URC. In this paper, we present and analyze the large-scale propagation characteristics of two different industrial environments -open production space and dense factory clutter -based on measurements conducted at 2.3 and 5.7 GHz By including a large number of spatially distributed samples, as per our experimental approach, we show the importance of properly characterizing the large-scale fading outage for URC. For instance, we show that based on a simple one-slope distance dependent path loss model, the conventional log-normal model for large-scale shadow fading is by far too simple for this environment. Our results show that at the 10 −4 percentile, the tail of the shadow fading distribution can deviate by up to 10-20 dB from the log-normal model with respect to the average NLOS values (around 6 dB and 8 dB at 2.3 and 5.7 GHz, respectively). The simplicity of the one-slope path loss model, and its ability as we show, to express the trends with respect to scenarios, frequencies, and antenna heights, makes it an attractable option. However, there is a need for further experimental insight, possibly in combination with deterministic analysis, to get a better understanding of the large-scale fading for the study of URC in industrial environments.

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Section: Introductionmentioning

confidence: 99%

Radio Propagation Analysis of Industrial Scenarios within the Context of Ultra-Reliable Communication

Wassie

Rodríguez

Berardinelli

et al. 2018

2018 IEEE 87th Vehicular Technology Conference (VTC Spring)

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“…For example, as shown in Fig. 1, the stripper-related XMEASs (XMEAS [15][16][17][18][19] share one wireless node at the same spot. However, each PV is allocated its own wireless bandwidth in a periodic manner to guarantee deterministic transmission within the network.…”

Section: B Network Model In the Te Plantmentioning

confidence: 99%

“…Other intermittent interference from nearby wireless nodes can be traced and mitigated through the co-existence solutions provided for example in [21], [22]. Additional information on wireless channel characterizations in the industrial plant environments can be found in [17], [18], [34].…”

Section: B Network Model In the Te Plantmentioning

confidence: 99%

“…Remley et al measured the wireless environment in a manufacturing plant and reported the significant differences compared to the indoor office environment [16]. Also based on field measurements, other research teams report vastly different radio wave propagation characteristics in different industrial applications depending on operating frequency [17] and factory topography [18]. Based on these findings, wireless solutions providing highly reliable and deterministic transmissions in wireless links for industrial control applications are proposed.…”

Section: Introductionmentioning

confidence: 99%

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A simulation framework for industrial wireless networks and process control systems

Liu

Candell

Lee

et al. 2016

2016 IEEE World Conference on Factory Communication Systems (WFCS)

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Factory and process automation systems are increasingly employing information and communications technologies to facilitate data sharing and analysis in integrated control operations. Wireless connections provide flexible access to a variety of field instruments and reduce network installation and maintenance costs. This serves as an incentive for the adoption of industrial wireless networks based on standards such as the WirelessHART and ISA100.11a in factory control systems. However, process control systems vary greatly and have diverse wireless networking requirements in different applications. These requirements include deterministic transmissions in the shared wireless bandwidth, low-cost operation, long-term durability, and high reliability in the harsh radio propagation environment.It is an open question whether a generic wireless technology would meet the requirements of industrial process control. In this paper, we propose a novel simulation framework for performance evaluation of wireless networks in factory and process automation systems. We select a typical process control plant model, specifically the Tennessee Eastman Challenge (TE) Model, and define the interfaces between the process simulator and the wireless network simulator. We develop a model of the protocol stack of the WirelessHART specification in the OMNET++ simulation engine as a typical industrial wireless network. We present simulation results that validate the prospect of using WirelessHART in the TE plant, and we evaluate the impact of various wireless network configurations on the plant operation. Given its modular design, the proposed simulation framework can be easily used to evaluate the performance of other industrial wireless networks in conjunction with a variety of process control systems.Index Terms-industrial wireless networks, sensor networks, factory and process automation networks, WirelessHART, network simulation.

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“…Multipath propagation has been studied and characterized in a multitude of environments, such as residential buildings, indoor offices and mines. The work performed in [6] proposes statistical models for estimating the root-mean-square (RMS) delay spread, path-loss and other relevant propagation [10] 1.89 0.5 298 Paper mill [10] 1.89 0.5 23 Subway tunnel [11] 2.4 0.1 159-234 Road tunnel [12] 5.2 0.1 20-100 Mine tunnel [13] 1 0.2 20-50 Mine tunnel [14] 2.4 0.5 14 Mine tunnel [15] 2.4 0.2 27.4 Mine tunnel [16] 2.4/5.8 0.2 1-15 Mine tunnel [17] 3.5 3 11-29 Mine tunnel [18] 2.4 0.2 1.7 LiU tunnel 3.5 2 3-16…”

Section: A Overview Of Measurements Campaignsmentioning

confidence: 99%

Measurement Analysis and Channel Modeling for TOA-Based Ranging in Tunnels

Savic

Ferrer-Coll

Ängskog

et al. 2015

IEEE Trans. Wireless Commun.

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Abstract-A robust and accurate positioning solution is required to increase the safety in GPS-denied environments. Although there is a lot of available research in this area, little has been done for confined environments such as tunnels. Therefore, we organized a measurement campaign in a basement tunnel of Linköping university, in which we obtained ultra-wideband (UWB) complex impulse responses for line-of-sight (LOS), and three non-LOS (NLOS) scenarios. This paper is focused on timeof-arrival (TOA) ranging since this technique can provide the most accurate range estimates, which are required for rangebased positioning. We describe the measurement setup and procedure, select the threshold for TOA estimation, analyze the channel propagation parameters obtained from the power delay profile (PDP), and provide statistical model for ranging. According to our results, the rise-time should be used for NLOS identification, and the maximum excess delay should be used for NLOS error mitigation. However, the NLOS condition cannot be perfectly determined, so the distance likelihood has to be represented in a Gaussian mixture form. We also compared these results with measurements from a mine tunnel, and found a similar behavior.

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Characterisation of highly absorbent and highly reflective radio wave propagation environments in industrial applications

Cited by 25 publications

References 15 publications

Radio Propagation Analysis of Industrial Scenarios within the Context of Ultra-Reliable Communication

Radio Propagation Analysis of Industrial Scenarios within the Context of Ultra-Reliable Communication

A simulation framework for industrial wireless networks and process control systems

Measurement Analysis and Channel Modeling for TOA-Based Ranging in Tunnels

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