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User Equipment Assisted Localization for 6G Integrated Sensing and Communication

Published 20 Dec 2023 in eess.SP, cs.IT, and math.IT | (2312.13013v2)

Abstract: This paper investigates user equipment (UE) assisted device-free networked sensing in the sixth-generation (6G) integrated sensing and communication (ISAC) system, where one base station (BS) and multiple UEs, such as unmanned aerial vehicles (UAVs), serve as anchors to cooperatively localize multiple passive targets based on the range information. Three challenges arise from the above scheme. First, the UEs are not perfectly synchronized with the BSs. Second, the UE (anchor) positions are usually estimated by the Global Positioning System (GPS) and subject to unknown errors. Third, data association is challenging, since it is hard for each anchor to associate each rang estimation to the right target under device-free sensing. We first tackle the above three challenges under a passive UE based sensing mode, where UEs only passively hear the signals over the BS-target-UE paths. A two-phase UE assisted localization protocol is proposed. In Phase I, we design an efficient method to accurately estimate the ranges from the BS to the targets and those from the BS to the targets to the UEs in the presence of synchronization errors between the BS and the UEs. In Phase II, an efficient algorithm is proposed to localize the targets via jointly removing the UEs with quite inaccurate position information from the anchor set and matching the estimated ranges at the BS and the remaining UEs with the targets. Next, we also consider an active UE based sensing mode, where the UEs can actively emit signals to obtain additional range information from them to the targets. We show that this additional range information can be utilized to significantly reduce the complexity of Phase II in the aforementioned two-phase localization protocol. Numerical results show that our proposed UE assisted networked sensing scheme can achieve very high localization accuracy.

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References (18)
  1. “Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond,” IEEE J. Sel. Areas Commun., vol. 40, no. 6, pp. 1728–1767, 2022.
  2. “Radar and communication coexistence: An overview: A review of recent methods,” IEEE Signal Process. Mag., vol. 36, no. 5, pp. 85–99, 2019.
  3. “An overview of signal processing techniques for joint communication and radar sensing,” IEEE J. Sel. Top. Signal Process., vol. 15, no. 6, pp. 1295–1315, 2021.
  4. “Dual-function radar communication systems: A solution to the spectrum congestion problem,” IEEE Signal Process. Mag., vol. 36, no. 5, pp. 115–126, 2019.
  5. “A survey on fundamental limits of integrated sensing and communication,” IEEE Commun. Surv. Tutorials, vol. 24, no. 2, pp. 994–1034, 2022.
  6. “Cooperative localization in wireless networks,” Proc. IEEE, vol. 97, no. 2, pp. 427–450, 2009.
  7. “Device-free sensing in OFDM cellular network,” IEEE J. Sel. Areas Commun., vol. 40, no. 6, pp. 1838–1853, 2022.
  8. X. Li, “An iterative NLOS mitigation algorithm for location estimation in sensor networks,” in Proc. 15th IST Mobile Wireless Commun. Summit, 2006, pp. 1–5.
  9. I. Guvenc and C. C. Chong, “A survey on TOA based wireless localization and NLOS mitigation techniques,” IEEE Commun. Surv. Tutorials, vol. 11, no. 3, pp. 107–124, 2009.
  10. “Distributed wireless sensor network localization via sequential greedy optimization algorithm,” IEEE Trans. Signal Process., vol. 58, no. 6, pp. 3328–3340, 2010.
  11. “Maximum entropy-based semi-definite programming for wireless sensor network localization,” IEEE Internet Things J., vol. 6, no. 2, pp. 3480–3491, 2019.
  12. “Semi-definite programming algorithms for sensor network node localization with uncertainties in anchor positions and/or propagation speed,” IEEE Trans. Signal Process., vol. 57, no. 2, pp. 752–763, 2009.
  13. “Second order cone programming for sensor network localization with anchor position uncertainty,” IEEE Trans. Wireless Commun., vol. 13, no. 2, pp. 749–763, 2014.
  14. “Cramér–Rao lower bounds of RSS-based localization with anchor position uncertainty,” IEEE Trans. Inf. Theory, vol. 61, no. 5, pp. 2807–2834, 2015.
  15. D. J. Torrieri, “Statistical theory of passive location systems,” IEEE Trans. Aerosp. Electron. Syst., vol. AES-20, no. 2, pp. 183–198, 1984.
  16. “Overview of radiolocation in CDMA cellular systems,” IEEE Commun. Mag., vol. 36, no. 4, pp. 38–45, 1998.
  17. P. C. Chen, “A non-line-of-sight error mitigation algorithm in location estimation,” in Proc. IEEE Int. Conf. Wireless Commun. Networking (WCNC), 1999, pp. 316–320.
  18. R. Zekavat and R. M. Buehrer, Handbook of position location: Theory, practice and advances, vol. 27, John Wiley & Sons, 2011.
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