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Downlink Multiuser Communications Relying on Flexible Intelligent Metasurfaces

Jiancheng An, Chau Yuen, Marco Di Renzo, Mérouane Debbah, H. Vincent Poor, Lajos Hanzo

TL;DR

This work tackles downlink power minimization in a multiuser MISO system by leveraging a flexible intelligent metasurface (FIM) at the base station to morph its surface shape for favorable beam steering. An alternating optimization framework jointly optimizes the transmit beamformers and the FIM surface, using MMSE/uplink-downlink concepts for beamforming and gradient-based surface morphing to progressively increase SINR margins. Numerical results show the FIM can yield about a 3 dB transmit-power reduction at a given data rate compared to a rigid 2D array, with larger morphing ranges and more scattering paths enhancing the gains. The study demonstrates potential energy-efficiency and spectrum-fatness benefits of FIMs in mmWave/THz networks, while noting challenges such as accurate channel estimation for real-world deployment.

Abstract

A flexible intelligent metasurface (FIM) is composed of an array of low-cost radiating elements, each of which can independently radiate electromagnetic signals and flexibly adjust its position through a 3D surface-morphing process. In our system, an FIM is deployed at a base station (BS) that transmits to multiple single-antenna users. We formulate an optimization problem for minimizing the total downlink transmit power at the BS by jointly optimizing the transmit beamforming and the FIM's surface shape, subject to an individual signal-to-interference-plus-noise ratio (SINR) constraint for each user as well as to a constraint on the maximum morphing range of the FIM. To address this problem, an efficient alternating optimization method is proposed to iteratively update the FIM's surface shape and the transmit beamformer to gradually reduce the transmit power. Finally, our simulation results show that at a given data rate the FIM reduces the transmit power by about $3$ dB compared to conventional rigid 2D arrays.

Downlink Multiuser Communications Relying on Flexible Intelligent Metasurfaces

TL;DR

This work tackles downlink power minimization in a multiuser MISO system by leveraging a flexible intelligent metasurface (FIM) at the base station to morph its surface shape for favorable beam steering. An alternating optimization framework jointly optimizes the transmit beamformers and the FIM surface, using MMSE/uplink-downlink concepts for beamforming and gradient-based surface morphing to progressively increase SINR margins. Numerical results show the FIM can yield about a 3 dB transmit-power reduction at a given data rate compared to a rigid 2D array, with larger morphing ranges and more scattering paths enhancing the gains. The study demonstrates potential energy-efficiency and spectrum-fatness benefits of FIMs in mmWave/THz networks, while noting challenges such as accurate channel estimation for real-world deployment.

Abstract

A flexible intelligent metasurface (FIM) is composed of an array of low-cost radiating elements, each of which can independently radiate electromagnetic signals and flexibly adjust its position through a 3D surface-morphing process. In our system, an FIM is deployed at a base station (BS) that transmits to multiple single-antenna users. We formulate an optimization problem for minimizing the total downlink transmit power at the BS by jointly optimizing the transmit beamforming and the FIM's surface shape, subject to an individual signal-to-interference-plus-noise ratio (SINR) constraint for each user as well as to a constraint on the maximum morphing range of the FIM. To address this problem, an efficient alternating optimization method is proposed to iteratively update the FIM's surface shape and the transmit beamformer to gradually reduce the transmit power. Finally, our simulation results show that at a given data rate the FIM reduces the transmit power by about dB compared to conventional rigid 2D arrays.

Paper Structure

This paper contains 10 sections, 19 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Problem Formulation and Solution
  4. Power Minimization Problem Formulation
  5. The Proposed Alternating Optimization Algorithm
  6. Transmit Beamforming Optimization $\left \{ \boldsymbol{w}_{k} \right \}$ Given the FIM's Surface Shape $\hat{\boldsymbol{y}}$
  7. Surface-Shape Morphing ${\boldsymbol{y}}$ Given the Beamforming Vectors $\left \{ {\hat{\boldsymbol{w}}_{k}} \right \}$
  8. ZF Beamforming Optimization
  9. Numerical Results
  10. Conclusions

Figures (4)

  • Figure 1: Schematic of a multiuser MISO system, where an FIM is deployed at the BS.
  • Figure 2: (a) Transmit power $P_{\textrm{t}}$ versus the SINR target $\gamma$; (b) Transmit power $P_{\textrm{t}}$ versus the number of propagation paths $L$.
  • Figure 3: (a) Transit power $P_{\textrm{t}}$ versus the morphing range of the FIM $\zeta$; (b) Convergence of the transmit power.
  • Figure 4: (a) Convergence of the FIM's surface shape ($L = 4$, and $\zeta = \lambda/2$); (b) Convergence of the FIM's surface shape ($L = 4$, and $\zeta = \lambda$).