Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

B-AWARE: Blockage Aware RSU Scheduling for 5G Enabled Autonomous Vehicles

Published: 09 September 2023 Publication History

Abstract

5G Millimeter Wave (mmWave) technology holds great promise for Connected Autonomous Vehicles (CAVs) due to its ability to achieve data rates in the Gbps range. However, mmWave suffers from a high beamforming overhead and requirement of line of sight (LOS) to maintain a strong connection. For Vehicle-to-Infrastructure (V2I) scenarios, where CAVs connect to roadside units (RSUs), these drawbacks become apparent. Because vehicles are dynamic, there is a large potential for link blockages. These blockages are detrimental to the connected applications running on the vehicle, such as cooperative perception and remote driver takeover. Existing RSU selection schemes base their decisions on signal strength and vehicle trajectory alone, which is not enough to prevent the blockage of links. Many modern CAVs motion planning algorithms routinely use other vehicle’s near-future path plans, either by explicit communication among vehicles, or by prediction. In this paper, we make use of the knowledge of other vehicle’s near future path plans to further improve the RSU association mechanism for CAVs. We solve the RSU association algorithm by converting it to a shortest path problem with the objective to maximize the total communication bandwidth. We evaluate our approach, titled B-AWARE, in simulation using Simulation of Urban Mobility (SUMO) and Digital twin for self-dRiving Intelligent VEhicles (DRIVE) on 12 highway and city street scenarios with varying traffic density and RSU placements. Simulations show B-AWARE results in a 1.05× improvement of the potential datarate in the average case and 1.28× in the best case vs. the state-of-the-art. But more impressively, B-AWARE reduces the time spent with no connection by 42% in the average case and 60% in the best case as compared to the state-of-the-art methods. This is a result of B-AWARE reducing nearly 100% of blockage occurrences.

References

[1]
3GPP TS 36.331. 2010. E-UTRA Radio Resource Control (RRC); Protocol specification (Release 9).
[2]
Adel Aldalbahi, Farzad Shahabi, and Mohammed Jasim. 2021. Instantaneous beam prediction scheme against link blockage in mmwave communications. Applied Sciences 11, 12 (2021), Art. no. 5601.
[3]
Ahmed Alkhateeb, Iz Beltagy, and Sam Alex. 2018. Machine learning for reliable mmWave systems: Blockage prediction and proactive handoff. In Proc. IEEE GlobalSIP. IEEE, New York, NY, USA, 1055–1059.
[4]
Edward Andert, Mohammad Khayatian, and Aviral Shrivastava. 2017. Crossroads: Time-sensitive autonomous intersection management technique. In Proceedings of the 54th Annual Design Automation Conference 2017. 1–6.
[5]
Edward Andert and Aviral Shrivastava. 2022. Accurate cooperative sensor fusion by parameterized covariance generation for sensing and localization pipelines in CAVs. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 3595–3602.
[6]
Marius Arvinte, Marcos Tavares, and Dragan Samardzija. 2019. Beam management in 5G NR using geolocation side information. In Proc. IEEE CISS. IEEE, New York, NY, USA, 1–6.
[7]
Mate Boban, Diego Dupleich, Naveed Iqbal, Jian Luo, Christian Schneider, Robert Müller, Ziming Yu, David Steer, Tommi Jämsä, Jian Li, et al. 2019. Multi-band vehicle-to-vehicle channel characterization in the presence of vehicle blockage. IEEE Access 7 (2019), 9724–9735.
[8]
Ananya Chattopadhyay, Aniruddha Chandra, and Chayanika Bose. 2021. Impact of RSU height on 60 GHz mmWave V2I LOS communication in multi-lane highways. In Proc. IEEE VTC2021-Spring. IEEE, New York, NY, USA, 1–5.
[9]
Sheng Chen, Kien Vu, Sheng Zhou, Zhisheng Niu, Mehdi Bennis, and Matti Latva-Aho. 2020. 1 A deep reinforcement learning framework to combat dynamic blockage in mmWave V2X networks. In Proc. IEEE 6G SUMMIT. IEEE, New York, NY, USA, 1–5.
[10]
Petrik Clarberg, Simon Kallweit, Craig Kolb, Pawel Kozlowski, Yong He, Lifan Wu, Edward Liu, Benedikt Bitterli, and Matt Pharr. 2022. Real-Time Path Tracing and Beyond. HPG 2022 Keynote.
[11]
Mohammed Dahhani, Gentian Jakllari, and André-Luc Beylot. 2019. Association and reliability in 802.11 ad networks: An experimental study. In Proc. IEEE LCN. IEEE, New York, NY, USA, 398–405.
[12]
[13]
Nil Garcia, Henk Wymeersch, Erik G. Ström, and Dirk Slock. 2016. Location-aided mm-wave channel estimation for vehicular communication. In Proc. IEEE SPAWC. IEEE, New York, NY, USA, 1–5.
[14]
Sanjay Goyal, Marco Mezzavilla, Sundeep Rangan, Shivendra Panwar, and Michele Zorzi. 2017. User association in 5G mmWave networks. In Proc. IEEE WCNC. IEEE, New York, NY, USA, 1–6.
[15]
Khondokar Fida Hasan, Yanming Feng, and Yu-Chu Tian. 2018. GNSS time synchronization in vehicular ad-hoc networks: Benefits and feasibility. IEEE Transactions on Intelligent Transportation Systems 19, 12 (2018), 3915–3924.
[16]
Ish Kumar Jain, Rajeev Kumar, and Shivendra S. Panwar. 2019. The impact of mobile blockers on millimeter wave cellular systems. IEEE Journal on Selected Areas in Communications 37, 4 (2019), 854–868.
[17]
Long Jiao, Pu Wang, Amir Alipour-Fanid, Huacheng Zeng, and Kai Zeng. 2021. Enabling efficient blockage-aware handover in RIS-assisted mmWave cellular networks. IEEE TWC 21, 4 (2021), 2243–2257.
[18]
Kishor Chandra Joshi, Rizqi Hersyandika, and R. Venkatesha Prasad. 2019. Association, blockage, and handoffs in IEEE 802.11 ad-Based 60-GHz picocells-a closer look. IEEE Systems Journal 14, 2 (2019), 2144–2153.
[19]
Wang Junsheng, Chen Yawen, Lu Zhaoming, Wen Xiangming, and Wang Zifan. 2019. A low-complexity beam searching method for fast handover in mmWave vehicular networks. In Proc. IEEE WCNCW. IEEE, New York, NY, USA, 1–6.
[20]
Mohammad Khayatian, Mohammadreza Mehrabian, Harshith Allamsetti, Kai Wei, Po Yu, Chung-Wei Lin, and Aviral Shrivastava. 2021. Cooperative driving of connected autonomous vehicles using responsibility-sensitive safety (RSS) rules, In Proceedings of the 12th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) (2021-04-10). ICCPS, 11–20. https://mpslab-asu.github.io/publications/papers/Khayatian2021ICCPS.pdf, paper https://mpslab-asu.github.io/publications/slides/Khayatian2021ICCPS.pptx,slide
[21]
Xiaotong Li, Ruiting Zhou, Ying-Jun Angela Zhang, Lei Jiao, and Zongpeng Li. 2020. Smart vehicular communication via 5G mmWaves. Computer Networks 172 (2020), 107173.
[22]
Zhiyuan Li and Weidong Wang. 2018. Handover performance in dense mmWave cellular networks. In Proc. IEEE WCSP. IEEE, New York, NY, USA, 1–7.
[23]
Jianbang Liu, Xinyu Mao, Yuqi Fang, Delong Zhu, and Max Q.-H. Meng. 2022. A survey on deep-learning approaches for vehicle trajectory prediction in autonomous driving. In Proc. IEEE Int. Conf. on Robotics and Biomimetics (ROBIO). IEEE Press, Sanya, China, 978–985.
[24]
Yi Lu, Mikhail Gerasimenko, Roman Kovalchukov, Martin Stusek, Jani Urama, Jiri Hosek, Mikko Valkama, and Elena Simona Lohan. 2020. Feasibility of location-aware handover for autonomous vehicles in industrial multi-radio environments. Sensors 20, 21 (2020), Art. no. 6290.
[25]
Ioannis Mavromatis, Robert J. Piechocki, Mahesh Sooriyabandara, and Arjun Parekh. 2020. Drive: A digital network oracle for cooperative intelligent transportation systems. In Proc. IEEE ISCC. IEEE, New York, NY, USA, 1–7.
[26]
Ioannis Mavromatis, Andrea Tassi, Robert J. Piechocki, and Andrew Nix. 2018. Efficient V2V communication scheme for 5G mmWave hyper-connected CAVs. In Proc. IEEE ICC Workshops. IEEE, New York, NY, USA, 1–6.
[27]
Marco Mezzavilla, Sanjay Goyal, Shivendra Panwar, Sundeep Rangan, and Michele Zorzi. 2016. An MDP model for optimal handover decisions in mmWave cellular networks. In Proc. IEEE EuCNC. IEEE, New York, NY, USA, 100–105.
[28]
João Morais, Arash Behboodi, Hamed Pezeshki, and Ahmed Alkhateeb. 2022. Position aided beam prediction in the real world: How useful GPS locations actually are? arXiv preprint arXiv:2205.09054 (2022).
[29]
Clare Mutzenich, Szonya Durant, Shaun Helman, and Polly Dalton. 2021. Updating our understanding of situation awareness in relation to remote operators of autonomous vehicles. Cognitive Research: Principles and Implications 6, 1 (2021), 1–17.
[30]
Raul Parada and Michele Zorzi. 2018. Context-aware handover in mmWave 5G using UE’s direction of pass. In Proc. EW. VDE, Catania, Italy, 1–6.
[31]
Hannah Parr, Catherine Harvey, and Gary Burnett. 2023. Investigating levels of remote operation in high-level on-road autonomous vehicles using operator sequence diagrams. DOI: (2023).
[32]
Michele Polese, Marco Giordani, Marco Mezzavilla, Sundeep Rangan, and Michele Zorzi. 2017. Improved handover through dual connectivity in 5G mmWave mobile networks. IEEE JSAC 35, 9 (2017), 2069–2084.
[33]
Bernhard Schulz. 2017. 802.11 ad–WLAN at 60 GHz–A Technology Introduction. White Paper.
[34]
Li Sun, Jing Hou, and Tao Shu. 2020. Spatial and temporal contextual multi-armed bandit handovers in ultra-dense mmWave cellular networks. IEEE TMC 20, 12 (2020), 3423–3438.
[35]
Yao Sun, Gang Feng, Shuang Qin, Ying-Chang Liang, and Tak-Shing Peter Yum. 2017. The SMART handoff policy for millimeter wave heterogeneous cellular networks. IEEE TMC 17, 6 (2017), 1456–1468.
[36]
Anup Talukdar, Mark Cudak, and Amitava Ghosh. 2014. Handoff rates for millimeterwave 5G systems. In Proc. IEEE VTC Spring. IEEE, New York, NY, USA, 1–5.
[37]
Robert Endre Tarjan. 1983. Data Structures and Network Algorithms. SIAM, Philadelphia, PA, USA.
[38]
Baidu Apollo team (2017). 2023. Apollo: Open Source Autonomous Driving. https://github.com/ApolloAuto/apollo
[39]
Caglar Tunc, Mustafa F Özkoç, Fraida Fund, and Shivendra S. Panwar. 2020. The blind side: Latency challenges in millimeter wave networks for connected vehicle applications. IEEE TVT 70, 1 (2020), 529–542.
[40]
Caglar Tunc and Shivendra S. Panwar. 2021. Mitigating the impact of blockages in millimeter-wave vehicular networks through vehicular relays. IEEE Open J. Intelligent Transp. Sys. 2 (2021), 225–239.
[41]
Aysenur Turkmen, Shuja Ansari, Paulo Valente Klaine, Lei Zhang, and Muhammad Ali Imran. 2021. IMPRESS: Indoor mobility prediction framework for pre-emptive indoor-outdoor handover for mmWave networks. IEEE Open J. Commun. Soc. 2 (2021), 2714–2724.
[42]
Nguyen Van Huynh, Diep N. Nguyen, Dinh Thai Hoang, and Eryk Dutkiewicz. 2021. Optimal beam association for high mobility mmwave vehicular networks: Lightweight parallel reinforcement learning approach. IEEE Transactions on Communications 69, 9 (2021), 5948–5961.
[43]
Song Wang, Jingqi Huang, and Xinyu Zhang. 2020. Demystifying millimeter-wave V2X: Towards robust and efficient directional connectivity under high mobility. In Proc. ACM MobiCom. ACM, New York, NY, USA.
[44]
Xiong Wang, Linghe Kong, Jintao Wu, Xiaofeng Gao, Hang Wang, and Guihai Chen. 2019. mmHandover: A pre-connection based handover protocol for 5G millimeter wave vehicular networks. In Proc. IEEE IWQoS. IEEE, New York, NY, USA, 1–10.
[45]
Atsushi Yamamoto, Koichi Ogawa, Tetsuo Horimatsu, Akihito Kato, and Masayuki Fujise. 2008. Path-loss prediction models for intervehicle communication at 60 GHz. IEEE Transactions on Vehicular Technology 57, 1 (2008), 65–78.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Embedded Computing Systems
ACM Transactions on Embedded Computing Systems  Volume 22, Issue 5s
Special Issue ESWEEK 2023
October 2023
1394 pages
ISSN:1539-9087
EISSN:1558-3465
DOI:10.1145/3614235
  • Editor:
  • Tulika Mitra
Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

Publication History

Published: 09 September 2023
Accepted: 30 June 2023
Revised: 02 June 2023
Received: 23 March 2023
Published in TECS Volume 22, Issue 5s

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. mmWave
  2. V2I
  3. CAV
  4. RSU
  5. selection
  6. user association
  7. autonomous vehicles
  8. 5G
  9. vehicular networks

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 461
    Total Downloads
  • Downloads (Last 12 months)307
  • Downloads (Last 6 weeks)27
Reflects downloads up to 23 Dec 2024

Other Metrics

Citations

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Full Text

View this article in Full Text.

Full Text

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media