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Joint Training and Reflection Pattern Optimization for Non-Ideal RIS-Aided Multiuser Systems (2403.19955v1)

Published 29 Mar 2024 in cs.IT and math.IT

Abstract: Reconfigurable intelligent surface (RIS) is a promising technique to improve the performance of future wireless communication systems at low energy consumption. To reap the potential benefits of RIS-aided beamforming, it is vital to enhance the accuracy of channel estimation. In this paper, we consider an RIS-aided multiuser system with non-ideal reflecting elements, each of which has a phase-dependent reflecting amplitude, and we aim to minimize the mean-squared error (MSE) of the channel estimation by jointly optimizing the training signals at the user equipments (UEs) and the reflection pattern at the RIS. As examples the least squares (LS) and linear minimum MSE (LMMSE) estimators are considered. The considered problems do not admit simple solution mainly due to the complicated constraints pertaining to the non-ideal RIS reflecting elements. As far as the LS criterion is concerned, we tackle this difficulty by first proving the optimality of orthogonal training symbols and then propose a majorization-minimization (MM)-based iterative method to design the reflection pattern, where a semi-closed form solution is obtained in each iteration. As for the LMMSE criterion, we address the joint training and reflection pattern optimization problem with an MM-based alternating algorithm, where a closed-form solution to the training symbols and a semi-closed form solution to the RIS reflecting coefficients are derived, respectively. Furthermore, an acceleration scheme is proposed to improve the convergence rate of the proposed MM algorithms. Finally, simulation results demonstrate the performance advantages of our proposed joint training and reflection pattern designs.

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References (35)
  1. Q. Wu and R. Zhang, “Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network,” IEEE Commun. Mag., vol. 58, no. 1, pp. 106–112, Jan. 2020.
  2. E. Basar, M. Di Renzo, J. de Rosny, M. Debbah, M.-S. Alouini, and R. Zhang, “Wireless communications through reconfigurable intelligent surfaces,” IEEE Access, vol. 7, pp. 116753–116773, 2019.
  3. W. Shi, W. Xu, X. You, C. Zhao, and K. Wei, “Intelligent reflection enabling technologies for integrated and green Internet-of-Everything beyond 5G: Communication, sensing, and security,” IEEE Wireless Commun., vol. 30, no. 2, pp. 147–154, Apr. 2023.
  4. Q. Wu and R. Zhang, “Intelligent reflecting surface enhanced wireless network: Joint active and passive beamforming design,” in Proc. IEEE Global Commun. Conf., Abu Dhabi, United Arab Emirates, Dec. 2018, pp. 1–6.
  5. Q. Wu and R. Zhang, “Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming,” IEEE Trans. Wireless Commun., vol. 18, no. 11, pp. 5394–5409, Nov. 2019.
  6. C. Huang, A. Zappone, M. Debbah, and C. Yuen, “Achievable rate maximization by passive intelligent mirrors,” in IEEE Int. Conf. Acoust., Speech and Signal Process. (ICASSP), Calgary, AB, Canada, Apr. 2018, pp. 1–5.
  7. C. Huang, A. Zappone, G. C. Alexandropoulos, M. Debbah, and C. Yuen, “Reconfigurable intelligent surfaces for energy efficiency in wireless communication,” IEEE Trans. Wireless Commun., vol. 18, no. 8, pp. 4157–4170, Aug. 2019.
  8. H. Shen, W. Xu, S. Gong, Z. He, and C. Zhao, “Secrecy rate maximization for intelligent reflecting surface assisted multi-antenna communications,” IEEE Commun. Lett., vol. 23, no. 9, pp. 1488–1492, Sep. 2019.
  9. X. Yu, D. Xu, and R. Schober, “Enabling secure wireless communications via intelligent reflecting surfaces,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Waikoloa, HI, USA, Dec. 2019, pp. 1–6.
  10. H. Shen, T. Ding, W. Xu, and C. Zhao, “Beamformig design with fast convergence for IRS-aided full-duplex communication,” IEEE Commun. Lett., vol. 24, no. 12, pp. 2849–2853, Dec. 2020.
  11. Q. Wu and R. Zhang, “Beamforming optimization for intelligent reflecting surface with discrete phase shifts,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Brighton, U.K., May 2019, pp. 7830–7833.
  12. C. Huang, G. C. Alexandropoulos, A. Zappone, M. Debbah, and C. Yuen, “Energy efficient multi-user MISO communication using low resolution large intelligent surfaces,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Abu Dhabi, United Arab Emirates, Dec. 2018, pp. 1–6.
  13. H. Shen, W. Xu, S. Gong, C. Zhao, and D. W. K. Ng, “Beamforming optimization for IRS-aided communications with transceiver hardware impairments,” IEEE Trans. Commun., vol. 69, no. 2, pp. 1214–1227, Feb. 2021.
  14. 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, Apr. 2020.
  15. S. Lin, B. Zheng, G. C. Alexandropoulos, M. Wen, F. Chen, and S. Mumtaz, “Adaptive transmission for reconfigurable intelligent surface-assisted OFDM wireless communications,” IEEE J. Sel. Areas Commun., vol. 38, no. 11, pp. 2653–2665, Nov. 2020.
  16. Z. He, H. Shen, W. Xu, and C. Zhao, “Low-cost passive beamforming for RIS-aided wideband OFDM systems,” IEEE Wireless Commun. Lett., vol. 11, no. 2, pp. 318–322, Feb. 2022.
  17. 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, Jul. 2020.
  18. C. You, B. Zheng, and R. Zhang, “Intelligent reflecting surface with discrete phase shifts: Channel estimation and passive beamforming,” in Proc. IEEE Int. Commun. Conf. Workshops (ICC Wkshps), Dublin, Ireland, Jun. 2020, pp. 1–6.
  19. Z. Zhou, N. Ge, Z. Wang, and L. Hanzo, “Joint transmit precoding and reconfigurable intelligent surface phase adjustment: A decomposition-aided channel estimation approach,” IEEE Trans. Commun., vol. 69, no. 2, pp. 1228–1243, Feb. 2021.
  20. J.-M. Kang, “Intelligent reflecting surface: Joint optimal training sequence and reflection pattern,” IEEE Commun. Lett., vol. 24, no. 8, pp. 1784–1788, Aug. 2020.
  21. J. Chen, Y.-C. Liang, H. V. Cheng, and W. Yu, “Channel estimation for reconfigurable intelligent surface aided multi-user mmWave MIMO systems,” IEEE Wireless Trans. Commun., vol. 22, no. 10, pp. 6853–6869, Oct. 2023.
  22. Q. Li, M. El-Hajjar, I. Hemadeh, A. Shojaeifard, and L. Hanzo, “Low-overhead channel estimation for RIS-aided multi-cell networks in the presence of phase quantization errors,” IEEE Trans. Veh. Technol., early access, Dec. 06, 2023, doi: 10.1109/TVT.2023.3339968.
  23. 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, Oct. 2020.
  24. M.-M. Zhao, Q. Wu, M.-J. Zhao, and R. Zhang, “Intelligent reflecting surface enhanced wireless networks: Two-timescale beamforming optimization,” IEEE Trans. Wireless Commun., vol. 20, no. 1, pp. 2–17, Jan. 2021.
  25. M. Di Renzo, F. H. Danufane, and S. Tretyakov, “Communication models for reconfigurable intelligent surfaces: From surface electromagnetics to wireless networks optimization,” Proc. IEEE, vol. 110, no. 9, pp. 1164–1209, Sep. 2022.
  26. S. Abeywickrama, R. Zhang, Q. Wu, and C. Yuen, “Intelligent reflecting surface: Practical phase shift model and beamforming optimization,” IEEE Trans. Commun., vol. 68, no. 9, pp. 5849–5863, Sep. 2020.
  27. 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.
  28. Y. Sun, P. Babu, and D. P. Palomar, “Majorization-minimization algorithms in signal processing, communications, and machine learning,” IEEE Trans. Signal Process., vol. 65, no. 3, pp. 794–816, Feb. 2017.
  29. D. P. Palomar, J. M. Cioffi, and M. A. Lagunas, “Joint Tx-Rx beamforming design for multicarrier MIMO channels: A unified framework for convex optimization,” IEEE Trans. Signal Process., vol. 51, no. 9, pp. 2381–2401, Sep. 2003.
  30. R. Varadhan and C. Roland, “Simple and globally convergent methods for accelerating the convergence of any EM algorithm,” Scand. J. Statist, vol. 35, no. 2, pp. 335–353, Feb. 2008.
  31. Z. Wang, P. Babu, and D. P. Palomar, “Design of PAR-constrained sequences for MIMO channel estimation via majorization-minimization,” IEEE Trans. Signal Process., vol. 64, no. 23, pp. 6132–6144, Dec. 2016.
  32. J. Song, P. Babu, and D. P. Palomar, “Sequence design to minimize the weighted integrated and peak sidelobe levels,” IEEE Trans. Signal Process., vol. 64, no. 8, pp. 2051–2064, Apr. 2016.
  33. S. Zhang and R. Zhang, “Capacity characterization for intelligent reflecting surface aided MIMO communication,” IEEE J. Sel. Areas Commun., vol. 38, no. 8, pp. 1823–1838, Aug. 2020.
  34. L. Zhao, J. Song, P. Babu, and D. P. Palomar, “A unified framework for low autocorrelation sequence design via majorization–minimization,” IEEE Trans. Signal Process., vol. 65, no. 2, pp. 438–453, Jan. 2017.
  35. D.-S. Shiu, G. Foschini, M. Gans, and J. Kahn, “Fading correlation and its effect on the capacity of multielement antenna systems,” IEEE Trans. Commun., vol. 48, no. 3, pp. 503–513, Mar. 2000.
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