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Near-Field Beamforming for Stacked Intelligent Metasurfaces-assisted MIMO Networks

Anastasios Papazafeiropoulos, Pandelis Kourtessis, Symeon Chatzinotas, Dimitra I. Kaklamani, Iakovos S. Venieris

Abstract

Stacked intelligent metasurfaces (SIMs) have recently gained significant interest since they enable precoding in the wave domain that comes with increased processing capability and reduced energy consumption. The study of SIMs and high frequency propagation make the study of the performance in the near field of crucial importance. Hence, in this work, we focus on SIM-assisted multiuser multiple-input multiple-output (MIMO) systems operating in the near field region. To this end, we formulate the weighted sum rate maximisation problem in terms of the transmit power and the phase shifts of the SIM. By applying a block coordinate descent (BCD)-relied algorithm, numerical results show the enhanced performance of the SIM in the near field with respect to the far field.

Near-Field Beamforming for Stacked Intelligent Metasurfaces-assisted MIMO Networks

Abstract

Stacked intelligent metasurfaces (SIMs) have recently gained significant interest since they enable precoding in the wave domain that comes with increased processing capability and reduced energy consumption. The study of SIMs and high frequency propagation make the study of the performance in the near field of crucial importance. Hence, in this work, we focus on SIM-assisted multiuser multiple-input multiple-output (MIMO) systems operating in the near field region. To this end, we formulate the weighted sum rate maximisation problem in terms of the transmit power and the phase shifts of the SIM. By applying a block coordinate descent (BCD)-relied algorithm, numerical results show the enhanced performance of the SIM in the near field with respect to the far field.
Paper Structure (16 sections, 1 theorem, 31 equations, 3 figures, 1 algorithm)

This paper contains 16 sections, 1 theorem, 31 equations, 3 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System and Channel Models
  3. System Model
  4. Channel Model
  5. Downlink Data Transmission and Achievable Rate
  6. Downlink Data Transmission
  7. Achievable Rate
  8. Problem Formulation and Optimization
  9. Problem Formulation
  10. Optimization with respect to ${\mathbf{U}}$
  11. Optimization with respect to ${\mathbf{Z}}$
  12. Optimization with respect to ${\mathbf{P}}$
  13. Optimization with respect to $\Phi$
  14. Overall Algorithm
  15. Numerical Results
  16. ...and 1 more sections

Key Result

Lemma 1

The complex gradient $\nabla_{{\bm{\phi}}_{l}} g_{i}({\bm{\phi}}_{l})$ is given in closed-form by

Figures (3)

  • Figure 1: A SIM-aided MIMO system in the near field.
  • Figure 2: Weighted sum rate of the SIM-aided MIMO architecture with respect to the number of meta-atoms $N$.
  • Figure 3: Weighted sum rate of the SIM-aided MIMO architecture with respect to the transmit power $P$.

Theorems & Definitions (5)

  • Remark 1
  • Remark 2
  • Remark 3
  • Lemma 1
  • proof