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Integrated Sensing and Communication for Vehicular Networks

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Integrated Sensing and Communications

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Abstract

In this chapter, we study the employment of integrated sensing and communication (ISAC) technology in vehicular networks, where vehicle tracking and vehicular communications can be combined for improving the overall system throughput. In the context of ISAC system, we develop a novel predictive beamforming scheme. In particular, the road-side unit (RSU) estimates and predicts the motion parameters of vehicles based on the echoes of the ISAC signal, which addresses the limitations of the conventional feedback-based beam tracking approaches, such as the high signaling overhead and low accuracy of angle estimation. A novel Bayesian inference scheme is proposed based on the vehicle state evolution model. Considering that the point-target assumption is impractical, we introduce the extended target model. Then, the beamwidth is adjusted in real-time to cover the entire vehicle. In addition, to improve the tracking accuracy and communication quality of service (QoS), we model the complicated roadway geometry via curvilinear coordinate system (CCS) and develop an interacting multiple model extended Kalman filter (IMM-EKF) framework. An optimization problem is formulated to maximize the array gain through dynamically adjusting the array size and thereby controlling the beamwidth, which takes the performance loss caused by beam misalignment into account. Numerical results have demonstrated the effectiveness of our proposed ISAC techniques for supporting vehicular communications.

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Notes

  1. 1.

    Note that \(\arctan ^{-1}(x) \in (-\pi /2,\pi /2) \), while the angle of vehicle is defined in the region of \([0,\pi ) \). For clarity, we define \(\tan ^{-1}\left( x \right) =\arctan \left( x \right) \) if \(x\ge 0\), otherwise \(\tan ^{-1}\left( x \right) =\arctan \left( x \right) + \pi \).

  2. 2.

    The first part with the wide beam can always cover the entire vehicle. Moreover, it contributes much less percent in the average achievable rate, so that we can use \(| \textbf{a}^H_n(\phi _n)\textbf{a}_n(\widehat{\phi }_{n|n-1}) |^2=1\) in the optimization problem for simplifications.

References

  1. S. Chen, J. Hu, Y. Shi, Y. Peng, J. Fang, R. Zhao, and L. Zhao, “Vehicle-to-everything (V2X) services supported by LTE-based systems and 5G,” IEEE Commun. Stand. Mag., vol. 1, no. 2, pp. 70–76, 2017.

    Article  Google Scholar 

  2. W. Yuan, S. Li, L. Xiang, and D. W. K. Ng, “Distributed estimation framework for beyond 5G intelligent vehicular networks,” IEEE Open Journal of Vehicular Technology, vol. 1, pp. 190–214, 2020.

    Article  Google Scholar 

  3. J. Z. Varghese, R. G. Boone et al., “Overview of autonomous vehicle sensors and systems,” in Proc. Int. Conf. Operations Excellence. Service Engineer., 2015, pp. 178–191.

    Google Scholar 

  4. J. Dickmann, J. Klappstein, M. Hahn, N. Appenrodt, H.-L. Bloecher, K. Werber, and A. Sailer, “Automotive radar the key technology for autonomous driving: From detection and ranging to environmental understanding,” in Proc. IEEE Radar Conf. IEEE, 2016, pp. 1–6.

    Google Scholar 

  5. Z. Wei, W. Yuan, S. Li, J. Yuan, G. Bharatula, R. Hadani, and L. Hanzo, “Orthogonal time-frequency space modulation: A promising next-generation waveform,” IEEE Wireless Communications, vol. 28, no. 4, pp. 136–144, 2021.

    Article  Google Scholar 

  6. F. Liu, Y. Cui, C. Masouros, J. Xu, T. X. Han, Y. C. Eldar, and S. Buzzi, “Integrated sensing and communications: Towards dual-functional wireless networks for 6g and beyond,” IEEE journal on selected areas in communications, 2022.

    Google Scholar 

  7. R. Zhang, B. Shim, W. Yuan, M. Di Renzo, X. Dang, and W. Wu, “Integrated sensing and communication waveform design with sparse vector coding: Low sidelobes and ultra reliability,” IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 4489–4494, 2022.

    Article  Google Scholar 

  8. S. Haghighatshoar and G. Caire, “The beam alignment problem in mmwave wireless networks,” in Proc. Asilomar Conf. IEEE, Jul. 2016, pp. 741–745.

    Google Scholar 

  9. V. Va, H. Vikalo, and R. W. Heath, “Beam tracking for mobile millimeter wave communication systems,” in Proc. IEEE Global Conf. Signal. Inf. Process. IEEE, 2016, pp. 743–747.

    Google Scholar 

  10. D. Zhang, A. Li, M. Shirvanimoghaddam, P. Cheng, Y. Li, and B. Vucetic, “Codebook-based training beam sequence design for millimeter-wave tracking systems,” IEEE Trans. Wireless Commun., vol. 18, no. 11, pp. 5333–5349, 2019.

    Article  Google Scholar 

  11. N. González-Prelcic, R. Méndez-Rial, and R. W. Heath, “Radar aided beam alignment in mmwave V2I communications supporting antenna diversity,” in Prof. Inf. Theory Applic. Workshop. IEEE, Jun. 2016, pp. 1–7.

    Google Scholar 

  12. F. Liu, W. Yuan, C. Masouros, and J. Yuan, “Radar-assisted predictive beamforming for vehicular links: Communication served by sensing,” IEEE Trans. Wireless Commun, vol. 19, no. 11, pp. 7704–7719, 2020.

    Article  Google Scholar 

  13. W. Yuan, F. Liu, C. Masouros, J. Yuan, D. W. K. Ng, and N. González-Prelcic, “Bayesian predictive beamforming for vehicular networks: A low-overhead joint radar-communication approach,” IEEE Transactions on Wireless Communications, vol. 20, no. 3, pp. 1442–1456, 2020.

    Article  Google Scholar 

  14. W. Yuan, Z. Wei, S. Li, J. Yuan, and D. W. K. Ng, “Integrated sensing and communication-assisted orthogonal time frequency space transmission for vehicular networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 6, pp. 1515–1528, 2021.

    Article  Google Scholar 

  15. C. Liu, W. Yuan, S. Li, X. Liu, H. Li, D. K. Ng, and Y. Li, “Learning-based predictive beamforming for integrated sensing and communication in vehicular networks,” IEEE Journal on Selected Areas in Communications, 2022.

    Google Scholar 

  16. S. Li, W. Yuan, C. Liu, Z. Wei, J. Yuan, B. Bai, and D. W. K. Ng, “A novel isac transmission framework based on spatially-spread orthogonal time frequency space modulation,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 6, pp. 1854–1872, 2022.

    Article  Google Scholar 

  17. M. A. Richards, Fundamentals of radar signal processing. McGraw-Hill Education, 2014.

    Google Scholar 

  18. L. Liu, S. Zhang, and R. Zhang, “CoMP in the sky: UAV placement and movement optimization for multi-user communications,” IEEE Trans. Commun, vol. 67, no. 8, pp. 5645–5658, 2019.

    Article  Google Scholar 

  19. S. Kay, Fundamentals of Statistical Signal Processing, Vol. I: Estimation Theory. Upper Saddle River, NJ: Prentice-Hall PTR, 1993.

    Google Scholar 

  20. Z. Du, F. Liu, W. Yuan, C. Masouros, Z. Zhang, and G. Caire, “Integrated sensing and communications for v2i networks: Dynamic predictive beamforming for extended vehicle targets,” arXiv preprint arXiv:2111.10152, 2021.

  21. H. L. Van Trees, Optimum array processing: Part IV of detection, estimation, and modulation theory. John Wiley & Sons, 2004.

    Google Scholar 

  22. D. Zhu, J. Choi, and R. W. Heath, “Auxiliary beam pair enabled AoD and AoA estimation in closed-loop large-scale millimeter-wave MIMO systems,” IEEE Trans. Wireless Commun, vol. 16, no. 7, pp. 4770–4785, 2017.

    Article  Google Scholar 

  23. K. Jo, M. Lee, J. Kim, and M. Sunwoo, “Tracking and behavior reasoning of moving vehicles based on roadway geometry constraints,” IEEE transactions on intelligent transportation systems, vol. 18, no. 2, pp. 460–476, 2016.

    Article  Google Scholar 

  24. H. Wang, J. Kearney, and K. Atkinson, “Arc-length parameterized spline curves for real-time simulation,” in Proc. 5th International Conference on Curves and Surfaces, vol. 387396, 2002.

    Google Scholar 

  25. H. Blom and Y. Bar-Shalom, “The interacting multiple model algorithm for systems with Markovian switching coefficients,” IEEE Transactions on Automatic Control, vol. 33, no. 8, pp. 780–783, 1988.

    Article  MATH  Google Scholar 

  26. F. Liu and C. Masouros, “A tutorial on joint radar and communication transmission for vehicular networks-part III: Predictive beamforming without state models,” IEEE Communications Letters, 2020.

    Google Scholar 

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Authors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Weijie Yuan & Fan Liu

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhen Du

  3. Beijing Institute of Technology, Beijing, China

    Xiao Meng

  4. University College London, London, UK

    Christos Masouros

Authors
  1. Weijie Yuan
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  2. Zhen Du
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  3. Xiao Meng
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  4. Fan Liu
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  5. Christos Masouros
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Correspondence to Weijie Yuan .

Editor information

Editors and Affiliations

  1. Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China

    Fan Liu

  2. Department of Electronic and Electrical Engineering, University College London, London, UK

    Christos Masouros

  3. Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel

    Yonina C. Eldar

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Yuan, W., Du, Z., Meng, X., Liu, F., Masouros, C. (2023). Integrated Sensing and Communication for Vehicular Networks. In: Liu, F., Masouros, C., Eldar, Y.C. (eds) Integrated Sensing and Communications. Springer, Singapore. https://doi.org/10.1007/978-981-99-2501-8_15

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  • DOI: https://doi.org/10.1007/978-981-99-2501-8_15

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