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Dual-Polarized Reconfigurable Intelligent Surface-Based Antenna for Holographic MIMO Communications

Shuhao Zeng, Hongliang Zhang, Boya Di, Zhu Han, H. Vincent Poor

TL;DR

An asymptotically tight upper bound on the ergodic capacity is derived on the ergodic capacity of a dual-polarized RIS-enabled single-user HMIMO network, based on which the power allocations across two polarizations are optimized.

Abstract

Holographic multiple-input-multiple output (HMIMO), which is enabled by large-scale antenna arrays with quasi-continuous apertures, is expected to be an important technology in the forthcoming 6G wireless network. Reconfigurable intelligent surface (RIS)-based antennas provide an energy-efficient solution for implementing HMIMO. Most existing works in this area focus on single-polarized RIS-enabled HMIMO, where the RIS can only reflect signals in one polarization towards users and signals in the other polarization cannot be received by intended users, leading to degraded data rate. To improve multiplexing performance, in this paper, we consider a dual-polarized RIS-enabled single-user HMIMO network, aiming to optimize power allocations across polarizations and analyze corresponding maximum system capacity. However, due to interference between different polarizations, the dual-polarized system cannot be simply decomposed into two independent single-polarized ones. Therefore, existing methods developed for the single-polarized system cannot be directly applied, which makes the optimization and analysis of the dual-polarized system challenging. To cope with this issue, we derive an asymptotically tight upper bound on the ergodic capacity, based on which the power allocations across two polarizations are optimized. Potential gains achievable with such dual-polarized RIS are analyzed. Numerical results verify our analysis.

Dual-Polarized Reconfigurable Intelligent Surface-Based Antenna for Holographic MIMO Communications

TL;DR

An asymptotically tight upper bound on the ergodic capacity is derived on the ergodic capacity of a dual-polarized RIS-enabled single-user HMIMO network, based on which the power allocations across two polarizations are optimized.

Abstract

Holographic multiple-input-multiple output (HMIMO), which is enabled by large-scale antenna arrays with quasi-continuous apertures, is expected to be an important technology in the forthcoming 6G wireless network. Reconfigurable intelligent surface (RIS)-based antennas provide an energy-efficient solution for implementing HMIMO. Most existing works in this area focus on single-polarized RIS-enabled HMIMO, where the RIS can only reflect signals in one polarization towards users and signals in the other polarization cannot be received by intended users, leading to degraded data rate. To improve multiplexing performance, in this paper, we consider a dual-polarized RIS-enabled single-user HMIMO network, aiming to optimize power allocations across polarizations and analyze corresponding maximum system capacity. However, due to interference between different polarizations, the dual-polarized system cannot be simply decomposed into two independent single-polarized ones. Therefore, existing methods developed for the single-polarized system cannot be directly applied, which makes the optimization and analysis of the dual-polarized system challenging. To cope with this issue, we derive an asymptotically tight upper bound on the ergodic capacity, based on which the power allocations across two polarizations are optimized. Potential gains achievable with such dual-polarized RIS are analyzed. Numerical results verify our analysis.
Paper Structure (20 sections, 83 equations, 9 figures, 1 table)

This paper contains 20 sections, 83 equations, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Scenario Description
  4. Dual-Polarized Reconfigurable Intelligent Surfaces
  5. Propagation Model from the Feed to the RIS
  6. Channel Model from the RIS to the UE
  7. Ergodic Capacity Analysis
  8. Upper Bound on the Ergodic Capacity
  9. Optimization of RIS Phase Shifts
  10. Power Allocation across Polarizations
  11. Comparison with Single-Polarized RIS-Aided System
  12. Simulation Results
  13. Conclusion
  14. Proof of Theorem \ref{['theorem_upper_bound']}
  15. Proof of Remark \ref{['remark_asymptotically_tight']}
  16. ...and 5 more sections

Figures (9)

  • Figure 1: System model of a dual-polarized downlink single-user network, where an RIS is deployed as the BS antenna.
  • Figure 2: Illustration of incident angles with respect to one $V$-polarized and one $H$-polarized RIS element.
  • Figure 3: System capacity versus the gain $\kappa$ of the feed, with the number of RIS elements $N_R=100$ and cross-polarization coefficient $l_{RU}=0.2$. $\rho$ represents the ratio between transmit power and noise power.
  • Figure 4: System capacity versus the number $N_R$ of RIS elements, with the gain of feed $\kappa=17$ dB and cross-polarization coefficient $l_{RU}=0.2$. $\rho$ represents the ratio between transmit power and noise power.
  • Figure 5: Performance comparison among different phase shift configuration schemes, with the transmit power $P=43$ dBm, the gain of the feed $\kappa=10$ dB, and cross-polarization coefficient $l_{RU}=0.2$.
  • ...and 4 more figures

Theorems & Definitions (9)

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