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Performance of Double-Stacked Intelligent Metasurface-Assisted Multiuser Massive MIMO Communications in the Wave Domain (2402.16405v1)

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

Abstract: Although reconfigurable intelligent surface (RIS) is a promising technology for shaping the propagation environment, it consists of a single-layer structure within inherent limitations regarding the number of beam steering patterns. Based on the recently revolutionary technology, denoted as stacked intelligent metasurface (SIM), we propose its implementation not only on the base station (BS) side in a massive multiple-input multiple-output (mMIMO) setup but also in the intermediate space between the base station and the users to adjust the environment further as needed. For the sake of convenience, we call the former BS SIM (BSIM), and the latter channel SIM (CSIM). Hence, we achieve wave-based combining at the BS and wave-based configuration at the intermediate space. Specifically, we propose a channel estimation method with reduced overhead, being crucial for SIMassisted communications. Next, we derive the uplink sum spectral efficiency (SE) in closed form in terms of statistical channel state information (CSI). Notably, we optimize the phase shifts of both BSIM and CSIM simultaneously by using the projected gradient ascent method (PGAM). Compared to previous works on SIMs, we study the uplink transmission, a mMIMO setup, channel estimation in a single phase, a second SIM at the intermediate space, and simultaneous optimization of the two SIMs. Simulation results show the impact of various parameters on the sum SE, and demonstrate the superiority of our optimization approach compared to the alternating optimization (AO) method.

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References (33)
  1. F. Boccardi et al., “Five disruptive technology directions for 5G,” IEEE Commun. Mag., vol. 52, no. 2, pp. 74–80, 2014.
  2. J. Zhang et al., “Prospective multiple antenna technologies for beyond 5G,” IEEE J. Sel. Areas Commun., vol. 38, no. 8, pp. 1637–1660, 2020.
  3. F. Sohrabi and W. Yu, “Hybrid digital and analog beamforming design for large-scale antenna arrays,” IEEE J. Sel. Top. Signal Proc., vol. 10, no. 3, pp. 501–513, 2016.
  4. T. S. Rappaport et al., “Wideband millimeter-wave propagation measurements and channel models for future wireless communication system design,” IEEE Trans. Commun., vol. 63, no. 9, pp. 3029–3056, 2015.
  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, 2019.
  6. E. Basar et al., “Wireless communications through reconfigurable intelligent surfaces,” IEEE Access, vol. 7, pp. 116 753–116 773, 2019.
  7. 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, 2021.
  8. A. Papazafeiropoulos et al., “Intelligent reflecting surface-assisted MU-MISO systems with imperfect hardware: Channel estimation and beamforming design,” IEEE Trans. Wireless Commun., vol. 21, no. 3, pp. 2077–2092, 2021.
  9. A. Papazafeiropoulos, “Ergodic capacity of IRS-assisted MIMO systems with correlation and practical phase-shift modeling,” IEEE Wireless Commun. Lett., vol. 11, no. 2, pp. 421–425, 2022.
  10. C. Huang et al., “Reconfigurable intelligent surfaces for energy efficiency in wireless communication,” IEEE Transa. Wireless Commun., vol. 18, no. 8, pp. 4157–4170, 2019.
  11. M. Di Renzo 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, 2020.
  12. Q. U. A. Nadeem et al., “Asymptotic max-min SINR analysis of reconfigurable intelligent surface assisted MISO systems,” IEEE Trans. Wireless Commun., vol. 19, no. 12, pp. 7748–7764, 2020.
  13. Y. Yang et al., “Intelligent reflecting surface meets OFDM: Protocol design and rate maximization,” IEEE Trans. Commun., vol. 68, no. 7, pp. 4522–4535, 2020.
  14. C. Pan et al., “Multicell MIMO communications relying on intelligent reflecting surfaces,” IEEE Trans. Wireless Commun., vol. 19, no. 8, pp. 5218–5233, 2020.
  15. A. Papazafeiropoulos et al., “Coverage probability of distributed IRS systems under spatially correlated channels,” IEEE Wireless Commun. Lett., vol. 10, no. 8, pp. 1722–1726, 2021.
  16. ——, “Achievable rate of a STAR-RIS assisted massive MIMO system under spatially-correlated channels,” IEEE Trans. Wireless Commun., pp. 1–1, 2023.
  17. A. Papazafeiropoulos, P. Kourtessis, and S. Chatzinotas, “Max-Min SINR analysis of STAR-RIS assisted massive MIMO systems with hardware impairments,” IEEE Trans. Wireless Commun., pp. 1–1, 2023.
  18. J. An et al., “Stacked intelligent metasurfaces for efficient holographic MIMO communications in 6G,” IEEE J. Sel. Areas Commun., 2023.
  19. X. Lin et al., “All-optical machine learning using diffractive deep neural networks,” Science, vol. 361, no. 6406, pp. 1004–1008, 2018.
  20. C. Liu et al., “A programmable diffractive deep neural network based on a digital-coding metasurface array,” Nature Electronics, vol. 5, no. 2, pp. 113–122, 2022.
  21. J. An et al., “Stacked intelligent metasurfaces for multiuser downlink beamforming in the wave domain,” arXiv preprint arXiv:2309.02687, 2023.
  22. 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, 2023.
  23. S. Abeywickrama et al., “Intelligent reflecting surface: Practical phase shift model and beamforming optimization,” IEEE Trans. Commun., vol. 68, no. 9, pp. 5849–5863, 2020.
  24. Q. Nadeem et al., “Intelligent reflecting surface-assisted multi-user MISO Communication: Channel estimation and beamforming design,” IEEE Open J. Commun. Soc., vol. 1, pp. 661–680, 2020.
  25. N. V. Deshpande et al., “Spatially-correlated irs-aided multiuser FD mMIMO systems: Analysis and optimization,” IEEE Trans. Commun., vol. 70, no. 6, pp. 3879–3896, 2022.
  26. C. Wu et al., “Channel estimation for STAR-RIS-aided wireless communication,” IEEE Commun. Letters, vol. 26, no. 3, pp. 652–656, 2021.
  27. B. Zheng et al., “A survey on channel estimation and practical passive beamforming design for intelligent reflecting surface aided wireless communications,” IEEE Commun. Sur. & Tut., vol. 24, no. 2, pp. 1035–1071, 2022.
  28. E. Björnson 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.
  29. J. Hoydis, S. ten Brink, and M. Debbah, “Massive MIMO in the UL/DL of cellular networks: How many antennas do we need?” IEEE J. Select. Areas Commun., vol. 31, no. 2, pp. 160–171, 2013.
  30. A. K. Papazafeiropoulos and T. Ratnarajah, “Deterministic equivalent performance analysis of time-varying massive MIMO systems,” IEEE Trans. Wireless Commun., vol. 14, no. 10, pp. 5795–5809, 2015.
  31. A. K. Papazafeiropoulos, “Impact of general channel aging conditions on the downlink performance of massive MIMO,” IEEE Trans. Veh. Tech., vol. 66, no. 2, pp. 1428–1442, Feb 2017.
  32. 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.
  33. N. S. Perović et al., “Achievable rate optimization for MIMO systems with reconfigurable intelligent surfaces,” IEEE Trans. Wireless Commun., vol. 20, no. 6, pp. 3865–3882, 2021.
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