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Channel Estimation for Stacked Intelligent Metasurface-Assisted Wireless Networks

Published 9 Mar 2024 in cs.IT, eess.SP, and math.IT | (2403.05870v1)

Abstract: Emerging technologies, such as holographic multiple-input multiple-output (HMIMO) and stacked intelligent metasurface (SIM), are driving the development of wireless communication systems. Specifically, the SIM is physically constructed by stacking multiple layers of metasurfaces and has an architecture similar to an artificial neural network (ANN), which can flexibly manipulate the electromagnetic waves that propagate through it at the speed of light. This architecture enables the SIM to achieve HMIMO precoding and combining in the wave domain, thus significantly reducing the hardware cost and energy consumption. In this letter, we investigate the channel estimation problem in SIM-assisted multi-user HMIMO communication systems. Since the number of antennas at the base station (BS) is much smaller than the number of meta-atoms per layer of the SIM, it is challenging to acquire the channel state information (CSI) in SIM-assisted multi-user systems. To address this issue, we collect multiple copies of the uplink pilot signals that propagate through the SIM. Furthermore, we leverage the array geometry to identify the subspace that spans arbitrary spatial correlation matrices. Based on partial CSI about the channel statistics, a pair of subspace-based channel estimators are proposed. Additionally, we compute the mean square error (MSE) of the proposed channel estimators and optimize the phase shifts of the SIM to minimize the MSE. Numerical results are illustrated to analyze the effectiveness of the proposed channel estimation schemes.

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References (23)
  1. J. An, C. Yuen, L. Dai, M. Di Renzo, M. Debbah, and L. Hanzo, “Toward beamfocusing-aided near-field communications: Research advances, potential, and challenges,” arXiv preprint arXiv:2309.09242, 2023.
  2. C.-X. Wang, X. You, X. Gao et al., “On the road to 6G: Visions, requirements, key technologies and testbeds,” IEEE Commun. Surv. Tutor., vol. 25, no. 2, pp. 905–974, Feb. 2023.
  3. F. Tang, X. Chen, M. Zhao, and N. Kato, “The roadmap of communication and networking in 6G for the metaverse,” IEEE Wireless Commun., vol. 30, no. 4, pp. 72–81, Aug. 2023.
  4. J. An, C. Yuen, C. Huang et al., “A tutorial on holographic MIMO communications—part I: Channel modeling and channel estimation,” IEEE Commun. Lett., vol. 27, no. 7, pp. 1664–1668, Jul. 2023.
  5. A. Pizzo, L. Sanguinetti, and T. L. Marzetta, “Fourier plane-wave series expansion for holographic MIMO communications,” IEEE Trans. Wireless Commun., vol. 21, no. 9, pp. 6890–6905, Sep. 2022.
  6. J. An, C. Yuen, C. Xu, H. Li, D. W. K. Ng, M. Di Renzo, M. Debbah, and L. Hanzo, “Stacked intelligent metasurface-aided mimo transceiver design,” arXiv preprint arXiv:2311.09814, 2023.
  7. C. Liu, Q. Ma, Z. J. Luo et al., “A programmable diffractive deep neural network based on a digital-coding metasurface array,” Nat. Electron., vol. 5, no. 2, pp. 113–122, Feb. 2022.
  8. R. Deng, Y. Zhang, H. Zhang et al., “Reconfigurable holographic surface: A new paradigm to implement holographic radio,” IEEE Veh. Technol. Mag., vol. 18, no. 1, pp. 20–28, Mar. 2023.
  9. C. Huang, S. Hu, G. C. Alexandropoulos et al., “Holographic MIMO surfaces for 6G wireless networks: Opportunities, challenges, and trends,” IEEE Wireless Commun., vol. 27, no. 5, pp. 118–125, Oct. 2020.
  10. J. An, M. Di Renzo, M. Debbah, and C. Yuen, “Stacked intelligent metasurfaces for multiuser beamforming in the wave domain,” in Proc. IEEE Int. Conf. Commun. (ICC), Rome, Italy, May. 2023, pp. 1–6.
  11. J. An, C. Xu, D. W. K. Ng et al., “Stacked intelligent metasurfaces for efficient holographic MIMO communications in 6G,” IEEE J. Sel. Areas Commun., vol. 41, no. 8, pp. 2380–2396, Aug. 2023.
  12. J. An, M. Di Renzo, M. Debbah, H. V. Poor, and C. Yuen, “Stacked intelligent metasurfaces for multiuser downlink beamforming in the wave domain,” arXiv preprint arXiv:2309.02687, 2023.
  13. X. Peng, L. Zhao, Y. Jiang et al., “Channel estimation for extremely large-scale massive MIMO systems in hybrid-field channel,” in 2023 IEEE/CIC International Conference on Communications in China (ICCC).   IEEE, Aug. 2023, pp. 1–6.
  14. M. Di Renzo, A. Zappone, M. Debbah et al., “Smart radio environments empowered by reconfigurable intelligent surfaces: How it works, state of research, and the road ahead,” IEEE J. Sel. Areas Commun., vol. 38, no. 11, pp. 2450–2525, Nov. 2020.
  15. X. Lin, Y. Rivenson, N. T. Yardimci et al., “All-optical machine learning using diffractive deep neural networks,” Sci., vol. 361, no. 6406, pp. 1004–1008, Jul. 2018.
  16. J. An, C. Yuen, Y. L. Guan, M. Di Renzo, M. Debbah, H. V. Poor, and L. Hanzo, “Two-dimensional direction-of-arrival estimation using stacked intelligent metasurfaces,” arXiv preprint arXiv:2402.08224, 2024.
  17. Ö. T. Demir, E. Björnson, and L. Sanguinetti, “Channel modeling and channel estimation for holographic massive MIMO with planar arrays,” IEEE Wireless Commun. Lett., vol. 11, no. 5, pp. 997–1001, Feb. 2022.
  18. C. Wei, H. Jiang, J. Dang et al., “Accurate channel estimation for mmwave massive MIMO with partially coherent phase offsets,” IEEE Commun. Lett., vol. 26, no. 9, pp. 2170–2174, Sep. 2022.
  19. E. Björnson and L. Sanguinetti, “Rayleigh fading modeling and channel hardening for reconfigurable intelligent surfaces,” IEEE Wireless Commun. Lett., vol. 10, no. 4, pp. 830–834, Dec. 2020.
  20. M. Biguesh and A. B. Gershman, “Training-based MIMO channel estimation: A study of estimator tradeoffs and optimal training signals,” IEEE Trans. Signal Process., vol. 54, no. 3, pp. 884–893, Mar. 2006.
  21. Q.-U.-A. Nadeem, J. An, and A. Chaaban, “Hybrid digital-wave domain channel estimator for stacked intelligent metasurface enabled multi-user MISO systems,” arXiv preprint arXiv:2309.16204, Sep. 2023.
  22. J. An, C. Xu, Q. Wu, D. W. K. Ng, M. D. Renzo, C. Yuen, and L. Hanzo, “Codebook-based solutions for reconfigurable intelligent surfaces and their open challenges,” IEEE Wireless Communications, pp. 1–8, 2022, Early Access.
  23. J. An, C. Xu, L. Gan, and L. Hanzo, “Low-complexity channel estimation and passive beamforming for RIS-assisted MIMO systems relying on discrete phase shifts,” IEEE Trans. Commun., vol. 70, no. 2, pp. 1245–1260, Feb. 2022.
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