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Channel Estimation and Beamforming for Beyond Diagonal Reconfigurable Intelligent Surfaces (2403.18087v2)

Published 26 Mar 2024 in eess.SP, cs.IT, and math.IT

Abstract: Beyond diagonal reconfigurable intelligent surface (BD-RIS) is a new advance and generalization of the RIS technique. BD-RIS breaks through the isolation between RIS elements by creatively introducing inter-element connections, thereby enabling smarter wave manipulation and enlarging coverage. However, exploring proper channel estimation schemes suitable for BD-RIS aided communication systems still remains an open problem. In this paper, we study channel estimation and beamforming design for BD-RIS aided multi-antenna systems. We first describe the channel estimation strategy based on the least square (LS) method, derive the mean square error (MSE) of the LS estimation, and formulate the joint pilot sequence and BD-RIS design problem with unique constraints induced by BD-RIS architectures. Specifically, we propose an efficient pilot sequence and BD-RIS design which theoretically guarantees to achieve the minimum MSE. With the estimated channel, we then consider two BD-RIS scenarios and propose beamforming design algorithms. Finally, we provide simulation results to verify the effectiveness of the proposed channel estimation scheme and beamforming design algorithms. We also show that more interelement connections in BD-RIS improves the performance while increasing the training overhead for channel estimation.

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References (29)
  1. H. Li, Y. Zhang, and B. Clerckx, “Channel estimation for beyond diagonal reconfigurable intelligent surfaces with group-connected architectures,” in IEEE 9th Int. Workshop on Computational Advances in Multi-Sensor Adaptive Process. (CAMSAP), 2023, pp. 21–25.
  2. M. Di Renzo, A. Zappone, M. Debbah, M.-S. Alouini, C. Yuen, J. De Rosny, and S. Tretyakov, “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, 2020.
  3. Q. Wu, S. Zhang, B. Zheng, C. You, and R. Zhang, “Intelligent reflecting surface-aided wireless communications: A tutorial,” IEEE Trans. Commun., vol. 69, no. 5, pp. 3313–3351, 2021.
  4. H. Li, S. Shen, M. Nerini, and B. Clerckx, “Reconfigurable intelligent surfaces 2.0: Beyond diagonal phase shift matrices,” IEEE Commun. Mag., 2023.
  5. S. Shen, B. Clerckx, and R. Murch, “Modeling and architecture design of reconfigurable intelligent surfaces using scattering parameter network analysis,” IEEE Trans. Wireless Commun., vol. 21, no. 2, pp. 1229–1243, 2022.
  6. M. Nerini, S. Shen, and B. Clerckx, “Discrete-value group and fully connected architectures for beyond diagonal reconfigurable intelligent surfaces,” IEEE Trans. Veh. Technol., 2023.
  7. ——, “Closed-form global optimization of beyond diagonal reconfigurable intelligent surfaces,” IEEE Trans. Wireless Commun., 2023.
  8. H. Li, S. Shen, and B. Clerckx, “Beyond diagonal reconfigurable intelligent surfaces: From transmitting and reflecting modes to single-, group-, and fully-connected architectures,” IEEE Trans. Wireless Commun., vol. 22, no. 4, pp. 2311–2324, 2023.
  9. ——, “Beyond diagonal reconfigurable intelligent surfaces: A multi-sector mode enabling highly directional full-space wireless coverage,” IEEE J. Sel. Areas Commnun., vol. 41, no. 8, pp. 2446–2460, 2023.
  10. M. Nerini, S. Shen, H. Li, and B. Clerckx, “Beyond diagonal reconfigurable intelligent surfaces utilizing graph theory: Modeling, architecture design, and optimization,” arXiv preprint arXiv:2305.05013, 2023.
  11. H. Zhang, S. Zeng, B. Di, Y. Tan, M. Di Renzo, M. Debbah, Z. Han, H. V. Poor, and L. Song, “Intelligent omni-surfaces for full-dimensional wireless communications: Principles, technology, and implementation,” IEEE Commun. Mag., vol. 60, no. 2, pp. 39–45, 2022.
  12. H. Zhang and B. Di, “Intelligent omni-surfaces: Simultaneous refraction and reflection for full-dimensional wireless communications,” IEEE Commun. Surveys & Tutorials, 2022.
  13. B. Zheng, C. You, W. Mei, and R. Zhang, “A survey on channel estimation and practical passive beamforming design for intelligent reflecting surface aided wireless communications,” IEEE Commun. Surveys & Tutorials, vol. 24, no. 2, pp. 1035–1071, 2022.
  14. X. Hu, R. Zhang, and C. Zhong, “Semi-passive elements assisted channel estimation for intelligent reflecting surface-aided communications,” IEEE Trans. Wireless Commun., vol. 21, no. 2, pp. 1132–1142, 2021.
  15. G. C. Alexandropoulos and E. Vlachos, “A hardware architecture for reconfigurable intelligent surfaces with minimal active elements for explicit channel estimation,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), 2020, pp. 9175–9179.
  16. A. Taha, M. Alrabeiah, and A. Alkhateeb, “Deep learning for large intelligent surfaces in millimeter wave and massive MIMO systems,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), 2019, pp. 1–6.
  17. A. L. Swindlehurst, G. Zhou, R. Liu, C. Pan, and M. Li, “Channel estimation with reconfigurable intelligent surfaces–A general framework,” Proc. IEEE, 2022.
  18. Y. Yang, B. Zheng, S. Zhang, and R. Zhang, “Intelligent reflecting surface meets OFDM: Protocol design and rate maximization,” IEEE Trans. Commun., vol. 68, no. 7, pp. 4522–4535, 2020.
  19. B. Zheng and R. Zhang, “Intelligent reflecting surface-enhanced OFDM: Channel estimation and reflection optimization,” IEEE Wireless Commun. Lett., vol. 9, no. 4, pp. 518–522, 2019.
  20. C. You, B. Zheng, and R. Zhang, “Channel estimation and passive beamforming for intelligent reflecting surface: Discrete phase shift and progressive refinement,” IEEE J. Sel. Areas Commun., vol. 38, no. 11, pp. 2604–2620, 2020.
  21. Z. Wang, L. Liu, and S. Cui, “Channel estimation for intelligent reflecting surface assisted multiuser communications: Framework, algorithms, and analysis,” IEEE Trans. Wireless Commun., vol. 19, no. 10, pp. 6607–6620, 2020.
  22. X. Guan, Q. Wu, and R. Zhang, “Anchor-assisted channel estimation for intelligent reflecting surface aided multiuser communication,” IEEE Trans. Wireless Commun., vol. 21, no. 6, pp. 3764–3778, 2021.
  23. G. Zhou, C. Pan, H. Ren, P. Popovski, and A. L. Swindlehurst, “Channel estimation for RIS-aided multiuser millimeter-wave systems,” IEEE Trans. Signal Process., vol. 70, pp. 1478–1492, 2022.
  24. N. K. Kundu, Z. Li, J. Rao, S. Shen, M. R. McKay, and R. Murch, “Optimal grouping strategy for reconfigurable intelligent surface assisted wireless communications,” IEEE Wireless Commun. Lett., vol. 11, no. 5, pp. 1082–1086, 2022.
  25. Z. Li, N. K. Kundu, J. Rao, S. Shen, M. R. McKay, and R. Murch, “Performance analysis of RIS-assisted communications with element grouping and spatial correlation,” IEEE Wireless Commun. Lett., vol. 12, no. 4, pp. 630–634, 2023.
  26. G. T. de Araújo, A. L. de Almeida, and R. Boyer, “Channel estimation for intelligent reflecting surface assisted MIMO systems: A tensor modeling approach,” IEEE J. Sel. Topics Signal Process., vol. 15, no. 3, pp. 789–802, 2021.
  27. P.-A. Absil, R. Mahony, and R. Sepulchre, “Optimization algorithms on matrix manifolds,” in Optimization Algorithms on Matrix Manifolds.   Princeton University Press, 2009.
  28. K. Shen and W. Yu, “Fractional programming for communication systems—Part I: Power control and beamforming,” IEEE Transactions on Signal Processing, vol. 66, no. 10, pp. 2616–2630, 2018.
  29. E. Björnson, J. Hoydis, L. Sanguinetti et al., “Massive MIMO networks: Spectral, energy, and hardware efficiency,” Foundations and Trends® in Signal Processing, vol. 11, no. 3-4, pp. 154–655, 2017.
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