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Open Access

Maximizing dirty-paper coding rate of RIS-assisted multi-user MIMO broadcast channels

Qualcomm Inc., Boxborough, MA 01719, USA
Electrical and Computer Engineering Department and the Computer Science Department, University of Houston, Houston, TX 77004, USA
Electrical and Computer Engineering Department, Manhattan College, Riverdale, NY 10471, USA
Electronics and Communications Engineering Department, American University in Cairo, New Cairo 11835, Egypt
Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK
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We consider a downlink multi-user scenario and investigate the use of reconfigurable intelligent surfaces (RISs) to maximize the dirty-paper-coding (DPC) sum rate of the RIS-assisted broadcast channel. Different from prior works, which maximize the rate achievable by linear precoders, we assume a capacity-achieving DPC scheme is employed at the transmitter and optimize the transmit covariances and RIS reflection coefficients to directly maximize the sum capacity of the broadcast channel. We propose an optimization algorithm that iteratively alternates between optimizing the transmit covariances using convex optimization and the RIS reflection coefficients using Riemannian manifold optimization. Our results show that the proposed technique can be used to effectively improve the sum capacity in a variety of scenarios compared to benchmark schemes.



E. Basar, M. D. Renzo, J. D. Rosny, M. Debbah, M. S. Alouini, and R. Zhang, Wireless communications through reconfigurable intelligent surfaces, IEEE Access, vol. 7, pp. 116753–116773, 2019.


M. A. Elmossallamy, H. Zhang, L. Song, K. G. Seddik, Z. Han, and G. Y. Li, Reconfigurable intelligent surfaces for wireless communications: Principles, challenges, and opportunities, IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 3, pp. 990–1002, 2020.

M. A. Elmossallamy, Passive reflection techniques for wireless communications, Doctoral dissertation, Electrical and Computer Engineering Department, University of Houston, Houston, TX, USA, 2020.

Q. Wu and R. Zhang, Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming, IEEE Transactions on Wireless Communications, vol. 18, no. 11, pp. 5394–5409, 2019.


H. Guo, Y. C. Liang, J. Chen, and E. G. Larsson, Weighted sum rate maximization for reconfigurable intelligent surface aided wireless networks, IEEE Transactions on Wireless Communications, vol. 19, no. 5, pp. 3064–3076, 2020.


C. Huang, R. Mo, and C. Yuen, Reconfigurable intelligent surface assisted multiuser MISO systems exploiting deep reinforcement learning, IEEE Journal on Selected Areas in Communications, vol. 38, no. 8, pp. 1839–1850, 2020.

A. Taha, M. Alrabeiah, and A. Alkhateeb, Deep learning for large intelligent surfaces in millimeter wave and massive MIMO systems, presented at 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 2019.

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 Transactions on Wireless Communications, vol. 19, no. 12, pp. 7748–7764, 2020.


Q. Wu and R. Zhang, Beamforming optimization for wireless network aided by intelligent reflecting surface with discrete phase shifts, IEEE Transactions on Communications, vol. 68, no. 3, pp. 1838–1851, 2020.


S. Vishwanath, N. Jindal, and A. Goldsmith, Duality, achievable rates, and sum rate capacity of Gaussian MIMO broadcast channels, IEEE Transactions on Information Theory, vol. 49, no. 10, pp. 2658–2668, 2003.

Z. He and X. Yuan, Cascaded channel estimation for large intelligent metasurface assisted massive mimo, IEEE Wireless Communications Letters, vol. 9, no. 2, pp. 210–214, 2020.

X. Wei, D. Shen, and L. Dai, Channel estimation for RIS assisted wireless communications—Part I: Fundamentals, solutions, and future opportunities, IEEE Communications Letters, vol. 25, no. 5, pp. 1398–1402, 2021.


D. P. Palomar and S. Verdu, Gradient of mutual information in linear vector Gaussian channels, IEEE Transactions on Information Theory, vol. 52, no. 1, pp. 141–154, 2006.

P. A. Absil, R. Mahony, and R. Sepulchre, Optimization Algorithms on Matrix Manifolds. Princeton, NJ, USA: Princeton University Press, 2008.

X. Yu, J. Shen, J. Zhang, and K. B. Letaief, Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems, IEEE Journal on Selected Areas in Communications, vol. 10, no. 3, pp. 485–500, 2016.

X. Yu, D. Xu, and R. Schober, MISO wireless communication systems via intelligent reflecting surfaces, in Proc. 2019 IEEE/CIC International Conference on Communications in China (ICCC), Changchun, China, 2019, pp. 735–740.

Z. J. Shi and J. Shen, Convergence of the Polak-Ribiére-Polyak conjugate gradient method, Nonlinear Analysis:Theory,Methods&Applications, vol. 66, no. 6, pp. 1428–1441, 2007.


Y. F. Hu and C. Storey, Global convergence result for conjugate gradient methods, Journal of Optimization Theory and Applications, vol. 71, no. 2, pp. 399–405, 1991.

A. I. Cohen, Rate of convergence of several conjugate gradient algorithms, SIAM Journal on Numerical Analysis, vol. 9, no. 2, pp. 248–259, 1972.
Further advancements for e-utra physical layer aspects (release 9), Tech. Rep. TR 36.814, 3GPP, 2010.

S. Zhang and R. Zhang, Capacity characterization for intelligent reflecting surface aided MIMO communication, IEEE Journal on Selected Areas in Communications, vol. 38, no. 8, pp. 1823–1838, 2020.

J. R. Shewchuk, An introduction to the conjugate gradient method without the agonizing pain, Tech. Rep., Carnegie Mellon University, Pittsburgh, PA, USA, 1994.
R. Hauser, The Conjugate Gradient Method, Lecture 5, Continuous Optimisation, Oxford University Computing Laboratory,, 2006.
Intelligent and Converged Networks
Pages 64-73
Cite this article:
Elmossallamy MA, Sultan R, Seddik KG, et al. Maximizing dirty-paper coding rate of RIS-assisted multi-user MIMO broadcast channels. Intelligent and Converged Networks, 2022, 3(1): 64-73.










Published: 30 March 2022
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