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Experimental Multiport-Network Parameter Estimation for a Dynamic Metasurface Antenna

Jean Tapie, Philipp del Hougne

TL;DR

This work addresses the challenge of forward-modeling dynamic metasurface antennas (DMAs) with strong inter-element mutual coupling by proposing an experimental procedure to estimate proxy multiport-network theory (MNT) parameters. The authors develop a two-phase identification method that decouples the estimation of feed-to-metal-element coupling, mutual coupling, and element-to-VAA coupling, enabling accurate end-to-end predictions of both the radiated field $\mathbf{y}_{\mathrm{G}}$ and the reflected field $\mathbf{y}_{\mathrm{F}}$ using a chaotic-cavity DMA prototype. The proxy MNT model achieves markedly higher accuracy than a MC-unaware benchmark (e.g., $\zeta_R^{\mathrm{MNT}}=40.3$ dB, $\zeta_T^{\mathrm{MNT}}=37.7$ dB vs $2.6$ dB and $3.3$ dB), and supports model-based DMA optimization for single- and dual-user beamforming; auxiliary calibration feeds further improve identifiability when operation uses fewer feeds. Overall, the approach enables robust, MC-aware forward modeling and end-to-end optimization of DMAs, with applicability to a range of reconfigurable antennas and non-invasive measurement regimes.

Abstract

Most use cases of reconfigurable antennas require an accurate forward model mapping configuration to radiated field (and reflections at feeds). Emerging dynamic metasurface antennas (DMAs) confront the conventional approach of extracting such a model from a numerical simulation with multiple challenges. First, the cost of accurately simulating an intricate and electrically large DMA architecture might be prohibitive. Second, the model-reality mismatch due to fabrication inaccuracies might be substantial, especially at higher frequencies and for DMA architectures leveraging strong inter-element mutual coupling (MC) to maximize their tunability. These considerations motivate an experimental parameter estimation for DMA forward models. The main challenge lies in the forward model's non-linearity due to inter-element MC. Multiport network theory (MNT) can accurately capture MC but the MC parameters cannot be measured directly. In this article, we demonstrate the experimental estimation of a high-accuracy proxy MNT model for a 19-GHz DMA with 7 feeds and 96 elements, where all feeds and elements are strongly coupled via a chaotic cavity. For a given DMA configuration and excitation, our proxy MNT model predicts the reflected field at the feeds and the radiated field with accuracies of 40.3 dB and 37.7 dB, respectively. A simpler, MC-unaware benchmark model only achieves 2.6 dB and 3.3 dB, respectively. We systematically examine the influence of the number of feeds and measured DMA configurations on the model accuracy, motivating the inclusion of "auxiliary calibration feeds" to facilitate the parameter estimation when the intended DMA operation is limited to a single feed. Finally, we measure DMA configurations optimized based on our proxy MNT model.

Experimental Multiport-Network Parameter Estimation for a Dynamic Metasurface Antenna

TL;DR

This work addresses the challenge of forward-modeling dynamic metasurface antennas (DMAs) with strong inter-element mutual coupling by proposing an experimental procedure to estimate proxy multiport-network theory (MNT) parameters. The authors develop a two-phase identification method that decouples the estimation of feed-to-metal-element coupling, mutual coupling, and element-to-VAA coupling, enabling accurate end-to-end predictions of both the radiated field and the reflected field using a chaotic-cavity DMA prototype. The proxy MNT model achieves markedly higher accuracy than a MC-unaware benchmark (e.g., dB, dB vs dB and dB), and supports model-based DMA optimization for single- and dual-user beamforming; auxiliary calibration feeds further improve identifiability when operation uses fewer feeds. Overall, the approach enables robust, MC-aware forward modeling and end-to-end optimization of DMAs, with applicability to a range of reconfigurable antennas and non-invasive measurement regimes.

Abstract

Most use cases of reconfigurable antennas require an accurate forward model mapping configuration to radiated field (and reflections at feeds). Emerging dynamic metasurface antennas (DMAs) confront the conventional approach of extracting such a model from a numerical simulation with multiple challenges. First, the cost of accurately simulating an intricate and electrically large DMA architecture might be prohibitive. Second, the model-reality mismatch due to fabrication inaccuracies might be substantial, especially at higher frequencies and for DMA architectures leveraging strong inter-element mutual coupling (MC) to maximize their tunability. These considerations motivate an experimental parameter estimation for DMA forward models. The main challenge lies in the forward model's non-linearity due to inter-element MC. Multiport network theory (MNT) can accurately capture MC but the MC parameters cannot be measured directly. In this article, we demonstrate the experimental estimation of a high-accuracy proxy MNT model for a 19-GHz DMA with 7 feeds and 96 elements, where all feeds and elements are strongly coupled via a chaotic cavity. For a given DMA configuration and excitation, our proxy MNT model predicts the reflected field at the feeds and the radiated field with accuracies of 40.3 dB and 37.7 dB, respectively. A simpler, MC-unaware benchmark model only achieves 2.6 dB and 3.3 dB, respectively. We systematically examine the influence of the number of feeds and measured DMA configurations on the model accuracy, motivating the inclusion of "auxiliary calibration feeds" to facilitate the parameter estimation when the intended DMA operation is limited to a single feed. Finally, we measure DMA configurations optimized based on our proxy MNT model.
Paper Structure (15 sections, 36 equations, 10 figures)

This paper contains 15 sections, 36 equations, 10 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. MNT Parameter Estimation Algorithm
  4. Background
  5. Step-by-step algorithm
  6. MC-unaware benchmark parameter estimation algorithm
  7. Experimental Validation
  8. DMA prototype
  9. Measurement setup and procedure
  10. System model sanity checks
  11. Model parameter estimation
  12. Model-Based DMA Optimization
  13. Conclusion
  14. Single-Toggle SVD Properties
  15. MC-Unaware Benchmark Model Parameter Estimation

Figures (10)

  • Figure 1: MNT system model for a DMA with $N_\mathrm{F}$ feeds and $N_\mathrm{M}$ reconfigurable meta-elements; the radiated field is probed by a VAA with $N_\mathrm{G}$ grid points.
  • Figure 2: Overview of mapping from control vector $\mathbf{b}$ and input signal $\mathbf{x}$ to radiated signal $\mathbf{y}_\mathrm{G}$ and reflected signal $\mathbf{y}_\mathrm{F}$. "Control circuit" refers to (\ref{['eq_b2r']}), "MNT" refers to (\ref{['eq_MNT']}), "Linear system response" refers to (\ref{['eq_LinSysResp']}).
  • Figure 3: Photographic and schematic views of our fabricated multi-port chaotic-cavity-based DMA. The front-view layout displays the locations of the seven feeds used in our experiments (blue), the 94 functioning meta-elements (green), the two faulty meta-elements (red), and the via fence (black).
  • Figure 4: Photographic and schematic view of our measurement setup. Lengths are indicated in mm in the schematic.
  • Figure 5: Sequence of measured DMA configurations. The vertical dimension corresponds to the meta-element index. Yellow and blue correspond to $b_i=0$ and $b_i=1$, respectively.
  • ...and 5 more figures