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Gulia, R.; Vashist, A.; Ganguly, A.; Hochgraf, C.; Kuhl, M.E. 60 GHz Wireless Connectivity for an Automated Warehouse. Encyclopedia. Available online: https://encyclopedia.pub/entry/49498 (accessed on 29 April 2024).
Gulia R, Vashist A, Ganguly A, Hochgraf C, Kuhl ME. 60 GHz Wireless Connectivity for an Automated Warehouse. Encyclopedia. Available at: https://encyclopedia.pub/entry/49498. Accessed April 29, 2024.
Gulia, Rahul, Abhishek Vashist, Amlan Ganguly, Clark Hochgraf, Michael E. Kuhl. "60 GHz Wireless Connectivity for an Automated Warehouse" Encyclopedia, https://encyclopedia.pub/entry/49498 (accessed April 29, 2024).
Gulia, R., Vashist, A., Ganguly, A., Hochgraf, C., & Kuhl, M.E. (2023, September 21). 60 GHz Wireless Connectivity for an Automated Warehouse. In Encyclopedia. https://encyclopedia.pub/entry/49498
Gulia, Rahul, et al. "60 GHz Wireless Connectivity for an Automated Warehouse." Encyclopedia. Web. 21 September, 2023.
60 GHz Wireless Connectivity for an Automated Warehouse
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This entry is adapted from the peer-reviewed paper 10.3390/info14090506

The fourth industrial revolution, often referred to as industry 4.0, envisions a technological transformation in industries, primarily centered around automated manufacturing. This transformation encompasses automated material handling, facilitated by smart interconnections among components, and the integration of artificial intelligence (AI) into production facilities.

automated warehouse industry 4.0 60 GHz propagation models SINR heatmaps

1. Introduction

The fourth industrial revolution, often referred to as industry 4.0, envisions a technological transformation in industries, primarily centered around automated manufacturing. This transformation encompasses automated material handling, facilitated by smart interconnections among components, and the integration of artificial intelligence (AI) into production facilities. Erboz [1] highlights the potential for enhancing efficiency in smart industries through self-monitoring systems. The convergence of machinery and the internet of things (IoT) empowers smart industries to autonomously analyze and diagnose issues within their operations. In the context of a smart warehouse, this entails the integration of multiple IoT sensors with automated forklifts, drones, and other mobile devices. To realize this fundamental shift in smart industrial environments, innovative wireless connectivity is imperative. Such connectivity should offer high bandwidth, flexibility, intelligence, speed, security, and low latency. Effective connectivity is pivotal across the entire supply chain, from production stations to customer orders, encompassing warehousing and delivery. It plays a pivotal role in ensuring the seamless delivery of consumer goods. Consequently, to enhance the operational efficiency of a warehouse, robust wireless connectivity is a prerequisite for the successful transition from a human-operated warehouse to an automated one.
Sophisticated path planning models have been developed to enhance the performance of autonomous material handling agents (AMHAs) within intricate warehouse environments, with the aim of improving delivery systems, as referenced in [2][3]. AMHAs represent autonomous forklifts or vehicles employed in smart warehouses, specifically designed for efficient material transport within various warehouse sections. When these transport vehicles, known as AMHAs, are operational in a smart warehouse, they are tasked with autonomous navigation. This entails the meticulous detection, prediction, tracking, and planning of paths, particularly in the presence of other autonomous vehicles operating in close proximity, as discussed in [4][5]. Such coordination requires extensive information exchange among AMHAs, necessitating the presence of robust wireless connectivity, as emphasized in [6].
The Federal Communications Commission (FCC) has designated an unlicensed spectrum ranging from 57 to 64 GHz for wireless communications. Within this 60 GHz millimeter-wave (mmWave) band, there exists significant potential for delivering exceptionally high speeds and robust channel capacity, catering to line-of-sight (LOS) as well as nLOS applications. The remarkable performance attributes of the 60 GHz band make it well suited to meet the connectivity demands of smart warehouses, thereby enhancing the performance of AMHAs and facilitating secure multi-tasking operations. Deployed alongside advanced antenna systems, this band leverages its distinctive propagation characteristics, including high oxygen absorption, excellent immunity to interference, heightened security, and frequency reuse, as elucidated in [7]. However, it is worth noting that mmWave frequency bands are susceptible to increased penetration and diffraction losses compared to lower frequency bands. Consequently, directional transmission becomes a critical feature within the 3rd Generation Partnership Project (3GPP) 5G new radio (NR) standard. In response, 3GPP has redesigned the antenna systems for mmWave frequency bands, with a focus on beam management, as detailed in [8][9], as well as beam measurement to support directional communication, as discussed in [10]. The intrinsic characteristics of the 60 GHz band, coupled with these advancements, make it capable of delivering a high quality of service (QoS) in smart industrial environments that rely on numerous machine-to-machine (M2M) communications and IoT sensors.

2. 60 GHz Wireless Connectivity for an Automated Warehouse

Channel modeling serves as a foundational element in the design of efficient wireless communication systems, and there has been notable research activity in this realm at the 60 GHz frequency band. One noteworthy endeavor involved obtaining real-world data through the use of a 60 GHz channel sounder to explore the application of the multiple-input multiple-output (MIMO) technique within indoor environments, as documented in [11]. The enhancement in multi-hop indoor wireless connectivity was achieved through the implementation of a diversity reception scheme, as discussed in [12]. Another innovative approach leveraged the physical characteristics of the uniform planar array (U-PA) and 2-dimensional discrete Fourier transform (2D-DFT) to introduce a novel channel estimation scheme tailored for 60 GHz massive MIMO systems, as outlined in [13]. Effectively transmitting multi-gigabit-per-second (Gb/s) customer data within a 60 GHz indoor communication system involved the application of various modulation schemes, as detailed in [14]. Furthermore, network performance within the 60 GHz frequency band was investigated in a smart warehouse context, without human intervention. This encompassed a comprehensive study of multipath components originating from metallic structures and various storage materials surrounding the access points (APs) and AMHAs, as elucidated in [15]. They conducted an extensive investigation into multipath components to discern the influence of metal frames within this dynamic network context, drawing comparisons between the 60 GHz and 5 GHz bands. However, this work does not demonstrate connectivity with fine-grained heatmaps.
In [16], channel characteristics were studied in a real warehouse environment at 2.4 GHz and 868 MHz to guarantee flexible and reliable connectivity in dynamic large-scale industrial applications, especially for connected warehouses. In [17], a 5G system architecture in smart factories was presented, focusing on ultra-reliable use cases at 28 GHz and 60 GHz. The study included channel modeling, ray tracing simulations, and a frequency comparison to highlight the potential of 60 GHz channels for reliable communication in industrial settings.
In [18], the manuscript delves into the propagation characteristics at 60 GHz within indoor environments, employing the shooting and bouncing ray tracing/image (SBR/IM) method. In [19], the analysis encompasses both LOS and nLOS scenarios, addressing omnidirectional path loss models and received power. Furthermore, a propagation measurement campaign at 60 GHz was conducted within an indoor office setting, leveraging a vector network analyzer (VNA) to exploit multipaths generated by various indoor elements, such as people, furnishings, and obstructions. In [20], the study encompasses 60 GHz channel sounding and throughput measurements, focusing on nLOS path characteristics in an indoor residential environment. In [21], the investigation extends to studying outdoor-to-indoor (O2I) penetration losses for frequencies ranging from 28 GHz to 73 GHz across various scenarios, utilizing the NYUSIM model. It scrutinizes the impact of diverse building materials (including glass, wood, IRR glass, and concrete) and antenna properties in O2I scenarios. In [22], for indoor-to-indoor and O2I scenarios, an office building was utilized to analyze penetration losses across different incident angles, underscoring the substantial penetration loss experienced by a 60 GHz channel in indoor environments. Additionally, in [23], the terragraph sounder (TG) channel sounder tool, developed by META, serves as a practical instrument for measuring physical channel properties. The investigations conducted encompass both LOS and nLOS scenarios, evaluating path loss and signal-to-interference-plus-noise ratio (SINR). To enhance the understanding of indoor propagation, a ray tracing simulator was discussed, leading to the creation of a data-driven indoor propagation model for multiple frequencies, as described in [24]. This model was subsequently employed with a convolutional encoder–decoder to predict path loss in uncharted areas of indoor environments.

References

  1. Erboz, G. How To Define Industry 4.0: Main Pillars of Industry 4.0. In Managerial Trends in the Development of Enterprises in Globalization Era; Slovak University of Agriculture: Nitra, Slovak, 2017; pp. 761–767. Available online: https://www.researchgate.net/publication/326557388_How_To_Define_Industry_40_Main_Pillars_Of_Industry_40. (accessed on 12 April 2023).
  2. Karaman, S.; Frazzoli, E. Sampling-based Algorithms for Optimal Motion Planning. Int. J. Robot. Res. 2011, 30, 846–894.
  3. Dolgov, D.; Thrun, S.; Montemerlo, M.; Diebel, J. Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments. Int. J. Robot. Res. 2010, 29, 485–501.
  4. Prakash, C.D.; Akhbari, F.; Karam, L.J. Robust obstacle detection for advanced driver assistance systems using distortions of inverse perspective mapping of a monocular camera. Robot. Auton. Syst. 2019, 114, 172–186.
  5. Brunetti, A.; Buongiorno, D.; Trotta, G.F.; Bevilacqua, V. Computer vision and deep learning techniques for pedestrian detection and tracking: A survey. Neurocomputing 2018, 300, 17–33.
  6. Santos, F.; Aquino, A.L.; Madeira, E.R.; Cabral, R.S. Temporal complex networks modeling applied to vehicular ad-hoc networks. J. Netw. Comput. Appl. 2021, 192, 103168.
  7. Richardson, A.; Watson, P. Use of the 55–65 GHz oxygen absorption band for short-range broadband radio networks with minimal regulatory control. IEE Proc. (Commun. Speech Vis.) 1990, 137, 233–241.
  8. NR-Physical channels and modulation-Release 15. In Technical Specification (TS) 38.211; 3rd Generation Partnership Project (3GPP); Version V15.0.0; 2018; Available online: https://www.etsi.org/deliver/etsi_ts/138200_138299/138211/15.02.00_60/ts_138211v150200p.pdf. (accessed on 13 June 2023).
  9. NR and NG-RAN Overall Description-Rel. 15. In Technical Specification (TS) 38.300; 3rd Generation Partnership Project (3GPP); Version V15.0.0; 2018; Available online: https://www.etsi.org/deliver/etsi_ts/138300_138399/138300/16.02.00_60/ts_138300v160200p.pdf (accessed on 13 June 2023).
  10. NR-Physical layer measurements-Rel. 15. In Technical Specification (TS) 38.215; 3rd Generation Partnership Project (3GPP); Version V15.0.0; 2017; Available online: https://www.etsi.org/deliver/etsi_ts/138200_138299/138215/17.03.00_60/ts_138215v170300p.pdf (accessed on 13 June 2023).
  11. Liu, P.; Blumenstein, J.; Perović, N.S.; Di Renzo, M.; Springer, A. Performance of Generalized Spatial Modulation MIMO Over Measured 60GHz Indoor Channels. IEEE Trans. Commun. 2018, 66, 133–148.
  12. Pitsiladis, G.; Panagopoulos, A.; Constantinou, P. Improving connectivity in indoor millimeter wave wireless networks using diversity reception. In Proceedings of the 6th European Conference on Antennas and Propagation (EUCAP), Prague, Czech Republic, 26–30 March 2012; pp. 510–514.
  13. Fan, D.; Gao, F.; Wang, G.; Zhong, Z. A 2D-DFT Based Channel Estimation Scheme in Indoor 60GHz Communication Systems with Large-Scale Multiple-Antenna. In Proceedings of the 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), Nanjing, China, 15–18 May 2016; pp. 1–5.
  14. Arya, P.; Huang, L.Y.; Liu, W.C.; Chang, H.T.; Jen, C.W.; Wu, C.F.; Jou, S.J. Gb/s prototyping of 60GHz indoor wireless SC/OFDM transmitter and receiver on FPGA demo system. In Proceedings of the 2015 IEEE International Conference on Consumer Electronics-Taiwan, Taipei, Taiwan, 6–8 June 2015; pp. 204–205.
  15. Gulia, R.S.; Mamun, S.A.; Vashist, A.; Ganguly, A.; Hochgraf, C.; Kwasinski, A.; Kuhl, M.E. Evaluation of Wireless Connectivity in an Automated Warehouse at 60 GHz. In Proceedings of the 2022 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 7–9 January 2022; pp. 1–6.
  16. Karaagac, A.; Haxhibeqiri, J.; Joseph, W.; Moerman, I.; Hoebeke, J. Wireless industrial communication for connected shuttle systems in warehouses. In Proceedings of the 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS), Trondheim, Norway, 31 May–2 June 2017; pp. 1–4.
  17. Yang, J.; Ai, B.; You, I.; Imran, M.; Wang, L.; Guan, K.; He, D.; Zhong, Z.; Keusgen, W. Ultra-Reliable Communications for Industrial Internet of Things: Design Considerations and Channel Modeling. IEEE Netw. 2019, 33, 104–111.
  18. Li, S.; Liu, Y.; Lin, L.; Sun, D.; Yang, S.; Sun, X. Simulation and Modeling of Millimeter-Wave Channel at 60 GHz in Indoor Environment for 5G Wireless Communication System. In Proceedings of the 2018 IEEE International Conference on Computational Electromagnetics (ICCEM), Chengdu, China, 26–28 March 2018; pp. 1–3.
  19. Hajj, M.E.; El Zein, G.; Zaharia, G.; Farhat, H.; Sadek, S. Angular Measurements and Analysis of the Indoor Propagation Channel at 60 GHz. In Proceedings of the 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Barcelona, Spain, 21–23 October 2019; pp. 121–126.
  20. Kacou, M.; Guillet, V.; El Zein, G.; Zaharia, G. Coverage and Throughput Analysis at 60 GHz for Indoor WLAN with Indirect Paths. In Proceedings of the 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Bologna, Italy, 9–12 September 2018; pp. 1–5.
  21. Zekri, A.B.; Ajgou, R.; Chemsa, A.; Ghendir, S. Analysis of Outdoor to Indoor Penetration Loss for mmWave Channels. In Proceedings of the 2020 1st International Conference on Communications, Control Systems and Signal Processing (CCSSP), El Oued, Algeria, 29 July 2020; pp. 74–79.
  22. Jun, S.Y.; Caudill, D.; Chuang, J.; Papazian, P.B.; Bodi, A.; Gentile, C.; Senic, J.; Golmie, N. Penetration Loss at 60 GHz for Indoor-to-Indoor and Outdoor-to-Indoor Mobile Scenarios. In Proceedings of the 2020 14th European Conference on Antennas and Propagation (EuCAP), Copenhagen, Denmark, 15–20 March 2020; pp. 1–5.
  23. Shkel, A.; Mehrabani, A.; Kusuma, J. A Configurable 60GHz Phased Array Platform for Multi-Link mmWave Channel Characterization. In Proceedings of the 2021 IEEE International Conference on Communications Workshops (ICC Workshops), Montreal, QC, Canada, 14–23 June 2021; pp. 1–6.
  24. Bakirtzis, S.; Chen, J.; Qiu, K.; Zhang, J.; Wassell, I. EM DeepRay: An Expedient, Generalizable, and Realistic Data-Driven Indoor Propagation Model. IEEE Trans. Antennas Propag. 2022, 70, 4140–4154.
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Subjects: Automation & Control Systems
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Rahul Gulia
,
Abhishek Vashist
,
Amlan Ganguly
,
Clark Hochgraf
,
Michael E. Kuhl
View Times: 112
Revisions: 2 times (View History)
Update Date: 22 Sep 2023
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