Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
194 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Efficient Design for Multi-user Downlink Beamforming with Reconfigurable Intelligent Surface (2401.00594v2)

Published 31 Dec 2023 in eess.SP, cs.IT, and math.IT

Abstract: This paper considers downlink multi-user transmission facilitated by a reconfigurable intelligent surface (RIS). First, focusing on the multi-group multicast beamforming scenario, we develop a fast and scalable algorithm for the joint base station (BS) and RIS beamforming optimization to minimize the transmit power subject to the user quality-of-service (QoS) constraints. By exploring the structure of this QoS problem, we show that the joint beamforming optimization can be naturally decomposed into a BS multicast beamforming QoS problem and an RIS passive multicast beamforming max-min-fair (MMF) problem. We propose an alternating multicast beamforming (AMBF) algorithm to solve the two subproblems alternatingly. For the BS QoS subproblem, we utilize the optimal multicast beamforming structure to obtain the BS beamformers efficiently. Furthermore, we reformulate the challenging RIS MMF subproblem and employ a first-order projected subgradient algorithm (PSA), which yields closed-form updates. The computational complexity of the AMBF algorithm grows linearly with the number of RIS elements and BS antennas. We further show that the AMBF approach is also an efficient method for the RIS-assisted downlink multi-user unicast beamforming problem, providing semi-closed-form updates. Next, we study the MMF problem for the RIS-assisted downlink beamforming design and propose a PSA-based fast algorithm to compute the BS and RIS beamforming solutions with closed-form updates per iteration, leading to a highly computationally efficient solution. Simulation results show the efficacy of our proposed algorithms in both performance and computational cost compared to other alternative methods.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (27)
  1. M. Ebrahimi and M. Dong, “Efficient design of multi-group multicast beamforming via reconfigurable intelligent surface,” in Proc. of Asilomar Conf. on Signals, Systems and Computers, Nov. 2023, pp. 1–5.
  2. Y. Liu, X. Liu, X. Mu, T. Hou, J. Xu, M. Di Renzo, and N. Al-Dhahir, “Reconfigurable intelligent surfaces: Principles and opportunities,” IEEE Communications Surveys & Tutorials, vol. 23, no. 3, pp. 1546–1577, May 2021.
  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. E. Björnson, H. Wymeersch, B. Matthiesen, P. Popovski, L. Sanguinetti, and E. de Carvalho, “Reconfigurable intelligent surfaces: A signal processing perspective with wireless applications,” IEEE Signal Process. Mag., vol. 39, no. 2, pp. 135–158, 2022.
  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, Aug. 2019.
  6. 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, Jun. 2019.
  7. H. Guo, Y.-C. Liang, J. Chen, and E. G. Larsson, “Weighted sum-rate maximization for reconfigurable intelligent surface aided wireless networks,” IEEE Trans. Wireless Commun., vol. 19, no. 5, pp. 3064–3076, Feb. 2020.
  8. N. Sidiropoulos, T. Davidson, and Z.-Q. Luo, “Transmit beamforming for physical-layer multicasting,” IEEE Trans. Signal Processing, vol. 54, pp. 2239–2251, Jun. 2006.
  9. D. Christopoulos, S. Chatzinotas, and B. Ottersten, “Weighted fair multicast multigroup beamforming under per-antenna power constraints,” IEEE Trans. Signal Process., vol. 62, pp. 5132–5142, Oct. 2014.
  10. L. N. Tran, M. F. Hanif, and M. Juntti, “A conic quadratic programming approach to physical layer multicasting for large-scale antenna arrays,” IEEE Signal Processing Letters, vol. 21, pp. 114–117, Jan 2014.
  11. M. Dong and Q. Wang, “Multi-group multicast beamforming: Optimal structure and efficient algorithms,” IEEE Trans. Signal Process., vol. 68, pp. 3738–3753, May 2020.
  12. C. Zhang, M. Dong, and B. Liang, “Fast first-order algorithm for large-scale max-min fair multi-group multicast beamforming,” IEEE Commun. Lett., vol. 11, no. 8, pp. 1560–1564, Apr. 2022.
  13. ——, “Ultra-low-complexity algorithms with structurally optimal multi-group multicast beamforming in large-scale systems,” IEEE Trans. Signal Process., vol. 71, pp. 1626–1641, Apr. 2023.
  14. S. Mohammadi, M. Dong, and S. ShahbazPanahi, “Fast algorithm for joint unicast and multicast beamforming for large-scale massive MIMO,” IEEE Trans. Signal Process., vol. 70, pp. 5413–5428, 2022.
  15. L. Du, S. Shao, G. Yang, J. Ma, Q. Liang, and Y. Tang, “Capacity characterization for reconfigurable intelligent surfaces assisted multiple-antenna multicast,” IEEE Trans. Wireless Commun., vol. 20, no. 10, pp. 6940–6953, May 2021.
  16. Q. Tao, S. Zhang, C. Zhong, and R. Zhang, “Intelligent reflecting surface aided multicasting with random passive beamforming,” IEEE Wireless Commun. Letters, vol. 10, no. 1, pp. 92–96, Sep. 2021.
  17. G. Zhou, C. Pan, H. Ren, K. Wang, and A. Nallanathan, “Intelligent reflecting surface aided multigroup multicast MISO communication systems,” IEEE Trans. Signal Process., vol. 68, pp. 3236–3251, Apr. 2020.
  18. M. Farooq, V. Kumar, M. Juntti, and L.-N. Tran, “On the achievable rate of IRS-assisted multigroup multicast systems,” in Proc.IEEE Global Telecommn. Conf. (GLOBECOM), Dec. 2022, pp. 5844–5849.
  19. D. Li, Q. An, Y. Shi, and Y. Zhou, “Multigroup multicast transmission via intelligent reflecting surface,” in Proc. IEEE Vehicular Technology Conf. (VTC), Feb. 2020, pp. 1–6.
  20. F. Shu, G. Yang, and Y.-C. Liang, “Reconfigurable intelligent surface enhanced symbiotic radio over multicasting signals,” in Proc. IEEE Vehicular Technology Conf. (VTC), Jun. 2021, pp. 1–6.
  21. M. Fu, Y. Zhou, and Y. Shi, “Intelligent reflecting surface for downlink non-orthogonal multiple access networks,” in 2019 IEEE Globecom Workshops (GC Wkshps), Mar. 2019, pp. 1–6.
  22. H. Yu, H. D. Tuan, A. A. Nasir, T. Q. Duong, and H. V. Poor, “Joint design of reconfigurable intelligent surfaces and transmit beamforming under proper and improper gaussian signaling,” IEEE J. Sel. Areas Commun., vol. 38, no. 11, pp. 2589–2603, Jul. 2020.
  23. Q.-U.-A. Nadeem, A. Kammoun, A. Chaaban, M. Debbah, and M.-S. Alouini, “Asymptotic max-min SINR analysis of reconfigurable intelligent surface assisted MISO systems,” IEEE Trans. Wireless Commun., vol. 19, no. 12, pp. 7748–7764, Apr. 2020.
  24. H. Xie, J. Xu, and Y.-F. Liu, “Max-min fairness in IRS-aided multi-cell MISO systems with joint transmit and reflective beamforming,” IEEE Trans. Wireless Commun., vol. 20, no. 2, pp. 1379–1393, 2021.
  25. C. Zhang, M. Dong, and B. Liang, “Ultra-low-complexity algorithms with structurally optimal multi-group multicast beamforming in large-scale systems,” IEEE Trans. Signal Processing, vol. 71, pp. 1626–1641, 2023.
  26. E. Björnson, M. Bengtsson, and B. Ottersten, “Optimal multiuser transmit beamforming: A difficult problem with a simple solution structure [lecture notes],” IEEE Signal Process. Mag., vol. 31, no. 4, pp. 142–148, Jun. 2014.
  27. E. Karipidis, N. Sidiropoulos, and Z.-Q. Luo, “Quality of service and max-min fair transmit beamforming to multiple cochannel multicast groups,” IEEE Trans. Signal Processing, vol. 56, pp. 1268–1279, 2008.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets