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Design and Operation Principles of a Wave-Controlled Reconfigurable Intelligent Surface

Published 3 Sep 2024 in eess.SY and cs.SY | (2409.01760v1)

Abstract: A Reflective Intelligent Surface (RIS) consists of many small reflective elements whose reflection properties can be adjusted to change the wireless propagation environment. Envisioned implementations require that each RIS element be connected to a controller, and as the number of RIS elements on a surface may be on the order of hundreds or more, the number of required electrical connectors creates a difficult wiring problem, especially at high frequencies where the physical space between the elements is limited. A potential solution to this problem was previously proposed by the authors in which "biasing transmission lines" carrying standing waves are sampled at each RIS location to produce the desired bias voltage for each RIS element. This solution has the potential to substantially reduce the complexity of the RIS control. This paper presents models for the RIS elements that account for mutual coupling and realistic varactor characteristics, as well as circuit models for sampling the transmission line to generate the RIS control signals. For the latter case, the paper investigates two techniques for conversion of the transmission line standing wave voltage to the varactor bias voltage, namely an envelope detector and a sample-and-hold circuit. The paper also develops a modal decomposition approach for generating standing waves that are able to generate beams and nulls in the resulting RIS radiation pattern that maximize either the Signal-to-Noise Ratio (SNR) or the Signal-to-Leakage-plus-Noise Ratio (SLNR). Extensive simulation results are provided for the two techniques, together with a discussion of computational complexity.

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References (21)
  1. C. Pan, G. Zhou, K. Zhi, S. Hong, T. Wu, Y. Pan, H. Ren, M. D. Renzo, A. Swindlehurst, R. Zhang, and A. Y. Zhang, “An overview of signal processing techniques for RIS/IRS-aided wireless systems,” IEEE J. Sel. Topics in Signal Processing, vol. 16, no. 5, pp. 883–917, 2022.
  2. A. Swindlehurst, G. Zhou, R. Liu, C. Pan, and M. Li, “Channel estimation with reconfigurable intelligent surfaces — A general framework,” Proceedings of the IEEE, vol. 110, no. 9, pp. 1312–1338, 2022.
  3. S. Xia, X. Chen, and H. Yin, “DFT based compression algorithm for configuration coefficients of reconfigurable intelligent surface,” in Proc. 13th Int’l Conf. on Wireless Communications and Signal Processing (WCSP), 2021.
  4. B. Sokal, P. R. B. Gomes, A. L. F. De Almeida, B. Makki, and G. Fodor, “Reducing the control overhead of intelligent reconfigurable surfaces via a tensor-based low-rank factorization approach,” IEEE Trans. Wireless Communications, 2023.
  5. A. Sayanskiy, A. Belov, R. Yafasov, A. Lyulyakin, A. Sherstobitov, S. Glybovski, and V. Lyashev, “A 2D-programmable and scalable reconfigurable intelligent surface remotely controlled via digital infrared code,” IEEE Trans. Antennas and Propagation, vol. 71, no. 1, pp. 570–580, 2023.
  6. S. Y. Miao and F. H. Lin, “Light-controlled large-scale wirelessly reconfigurable microstrip reflectarrays,” IEEE Trans. Antennas and Propagation, vol. 71, no. 2, pp. 1613–1622, 2023.
  7. E. Ayanoglu, F. Capolino, and A. L. Swindlehurst, “Wave-controlled metasurface-based reconfigurable intelligent surfaces,” IEEE Wireless Communications, vol. 29, no. 4, pp. 86–92, August 2022.
  8. T. Sleasman, M. F. Imani, W. Xu, J. Hunt, T. Driscoll, M. S. Reynolds, and D. R. Smith, “Waveguide-fed tunable metamaterial element for dynamic apertures,” IEEE Antennas and Wireless Propagation Letters, vol. 15, pp. 606–609, 2016.
  9. N. Shlezinger, G. C. Alexandropoulos, M. F. Imani, Y. C. Eldar, and D. R. Smith, “Dynamic metasurface antennas for 6G extreme massive MIMO communications,” IEEE Wireless Communications, vol. 28, no. 2, pp. 106–113, 2021.
  10. D. Hanna, M. Saavedra-Melo, F. Shan, and F. Capolino, “A versatile polynomial model for reflection by a reflective intelligent surface with varactors,” in IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022, pp. 679–680.
  11. F. Costa and M. Borgese, “Electromagnetic model of reflective intelligent surfaces,” IEEE Open Journal of the Communications Society, vol. 2, pp. 1577–1589, 2021.
  12. P. Nayeri, F. Yang, and A. Z. Elsherbeni, “Beam‐Scanning Reflectarray Antennas,” in Reflectarray Antennas: Theory, Designs, and Applications, 2018, pp. 303–366.
  13. F. Capolino, A. Vallecchi, and M. Albani, “Equivalent transmission line model with a lumped X-circuit for a metalayer made of pairs of planar conductors,” IEEE Trans. on Antennas and Propagation, vol. 61, no. 2, pp. 852–861, 2013.
  14. D. Sievenpiper, L. Zhang, R. Broas, N. Alexopolous, and E. Yablonovitch, “High-impedance electromagnetic surfaces with a forbidden frequency band,” IEEE Trans. on Microwave Theory and Techniques, vol. 47, no. 11, pp. 2059–2074, 1999.
  15. S. R. Best and D. L. Hanna, “Design of a broadband dipole in close proximity to an EBG ground plane,” IEEE Antennas and Propagation Magazine, vol. 50, no. 6, pp. 52–64, 2008.
  16. M. Saavedra-Melo, K. Rouhi, and F. Capolino, “Wave-controlled RIS: A novel method for reconfigurable elements biasing,” in 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), 2023, pp. 979–980.
  17. I. Vardakis, G. Kotridis, S. Peppas, K. Skyvalakis, G. Vougioukas, and A. Bletsas, “Intelligently wireless batteryless RF-powered reconfigurable surface: Theory, implementation & limitations,” IEEE Transactions on Wireless Communications, vol. 22, no. 6, pp. 3942–3954, Jun. 2023.
  18. Z. Mao, W. Wang, Q. Xia, C. Huang, X. Pan, and Z. Ye, “Amplitude-Dependent Phase-Gradient Directional Beamforming for IRS: A Scalable Optimization Framework,” IEEE Transactions on Communications, vol. 72, no. 4, pp. 2354–2369, 2024.
  19. Y. Plan and R. Vershynin, “The generalized lasso with non-linear observations,” IEEE Transactions on Information Theory, pp. 1528–1537, March 2016.
  20. V. Cerny, “Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm,” Journal of Optimization Theory and Applications, vol. 45, pp. 41–51, January 1985.
  21. Z. Yahong, L. Cuiran, and L. Yongje, “Simulated annealing genetic algorithm based ris phase optimization in the high-speed communication of the railways,” in 2023 8th International Conference on Communication, Image and Signal Processing (CCISP), 2023, pp. 1–5.

Summary

  • The paper introduces a novel approach using standing waves to generate bias voltages, significantly reducing wiring complexity.
  • It employs varactor-based control and full-wave simulations to accurately tune reflection phase and amplitude on RIS elements.
  • Optimization techniques including envelope detection and sample-and-hold methods validate its potential for enhanced signal distribution.

Design and Operation Principles of a Wave-Controlled Reconfigurable Intelligent Surface

Introduction

The paper discusses the development of a wave-controlled Reconfigurable Intelligent Surface (RIS) that employs standing wave voltages on transmission lines to control the reflection properties of surface elements. This architecture simplifies the complex wiring typically needed for RIS control, potentially reducing the amount of configuration information required.

Architectural Overview

The proposed RIS architecture consists of metallic patches whose reflective properties are controlled by varying the biasing voltage across varactors. These varactors are connected to transmission lines excited with standing waves. The architecture requires only one electrical connection per row, significantly reducing complexity compared to traditional designs where each element needs individual control.

Varactor-Based RIS Design

A crucial component of this RIS design is its varactor-based control mechanism. Varactors are chosen based on low resistance and inductance to efficiently tune the reflection coefficient phases and amplitudes of the RIS elements. The paper provides an analytical model for the RIS elements, considering mutual coupling effects and material losses, verified by full-wave electromagnetic simulations.

Transmission Line Configuration

The key innovation is using low-frequency standing waves on a transmission line to generate the necessary biasing voltages. This method reduces the need for dense wiring by leveraging spatial Fourier modes to control the RIS. A mix of full-domain standing waves is employed to induce bias voltages across RIS elements. By sampling the standing waveforms, unique voltage distributions are created, dictating the phase shifts necessary for desired RIS performance.

Optimization Techniques

The paper details optimization algorithms for configuring the RIS to achieve desired signal power distributions. Simulation results demonstrate use cases like maximizing signal-to-noise ratio (SNR) or signal-to-leakage-plus-noise ratio (SLNR) according to specific communication goals. The paper discusses two methods for voltage conversion: envelope detectors, which simply rectify the AC waveform, and sample-and-hold circuits, which periodically sample and maintain voltage levels.

Envelope Detector Analysis

The envelope detector model rectifies the sinusoidal waveform from the transmission line and is computationally simpler but offers less precision in configuring the reflection phases compared to sample-and-hold circuits. Optimization strategies using this model involve iterative algorithms to adjust mode amplitudes for directional beamforming.

Sample-and-Hold Circuit Implementation

The sample-and-hold model provides finer control over the varactor biasing voltages by holding the sampled waveform value. This model allows applying advanced optimization techniques using least squares and simulated annealing for configuring the RIS, resulting in better performance in scenarios requiring complex beam patterns.

Applications and Performance

The RIS exhibits promising capabilities in terms of dynamically shaping wireless communication environments, with potential applications ranging from interference mitigation to targeted signal enhancement for specific users. The power efficiency and scalability of this approach make it especially suitable for high-frequency applications where traditional wiring is untenable.

Conclusion

The wave-controlled RIS offers a viable solution to the problem of complex control circuitry required for large-scale intelligent surfaces. By harnessing standing waves, this architecture simplifies the deployment and operation of RIS in various wireless communication scenarios, delivering reliable performance with reduced overhead and resource usage. Future work may focus on refining these techniques and exploring new optimization methods to further enhance system capabilities.

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