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Joint Communication and Sensing in RIS-Assisted MIMO System Under Mutual Coupling

Dilki Wijekoon, Amine Mezghani, Ekram Hossain

Abstract

This paper considers a downlink Reconfigurable Intelligent Surface (RIS)-assisted Joint Communication and Sensing (JCAS) system within a physically-consistent setting, accounting for the effect of mutual coupling between RIS elements arising due to sub-element spacing. The system features a multiple-input multiple-output (MIMO) terrestrial base station (BS) and explores both monostatic and bistatic radar configurations to enable joint communication and sensing. In the monostatic configuration, both the transmitter and receiver are at the same location, while the bistatic configuration separates the transmitter and receiver spatially. System performance is evaluated using Fisher Information (FI) to quantify sensing accuracy and Mutual Information (MI) to measure communication efficiency. To achieve an optimal balance between communication and sensing, the RIS reflective coefficients and BS transmit beamforming are jointly optimized by maximizing a weighted sum of FI and MI. A novel solution approach is proposed for a single-user, single-object scenario, leveraging the mutual coupling model to enhance system realism. The impact of self-interference on sensing performance is also investigated through signal quantization. Numerical results reveal a fundamental trade-off between FI and MI and demonstrate that incorporating mutual coupling within a physically-consistent framework significantly improves both communication and sensing performance compared to conventional RIS-assisted JCAS models. Additionally, the analysis highlights how the choice of monostatic versus bistatic radar configuration affects system performance, offering valuable insights for the design of RIS-assisted JCAS systems.

Joint Communication and Sensing in RIS-Assisted MIMO System Under Mutual Coupling

Abstract

This paper considers a downlink Reconfigurable Intelligent Surface (RIS)-assisted Joint Communication and Sensing (JCAS) system within a physically-consistent setting, accounting for the effect of mutual coupling between RIS elements arising due to sub-element spacing. The system features a multiple-input multiple-output (MIMO) terrestrial base station (BS) and explores both monostatic and bistatic radar configurations to enable joint communication and sensing. In the monostatic configuration, both the transmitter and receiver are at the same location, while the bistatic configuration separates the transmitter and receiver spatially. System performance is evaluated using Fisher Information (FI) to quantify sensing accuracy and Mutual Information (MI) to measure communication efficiency. To achieve an optimal balance between communication and sensing, the RIS reflective coefficients and BS transmit beamforming are jointly optimized by maximizing a weighted sum of FI and MI. A novel solution approach is proposed for a single-user, single-object scenario, leveraging the mutual coupling model to enhance system realism. The impact of self-interference on sensing performance is also investigated through signal quantization. Numerical results reveal a fundamental trade-off between FI and MI and demonstrate that incorporating mutual coupling within a physically-consistent framework significantly improves both communication and sensing performance compared to conventional RIS-assisted JCAS models. Additionally, the analysis highlights how the choice of monostatic versus bistatic radar configuration affects system performance, offering valuable insights for the design of RIS-assisted JCAS systems.
Paper Structure (18 sections, 56 equations, 11 figures, 1 table, 1 algorithm)

This paper contains 18 sections, 56 equations, 11 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Contributions
  4. System Model and Assumptions
  5. Communication Model
  6. Sensing Model for Monostatic Radar
  7. Sensing Model for Bistatic Radar
  8. Effect of Self-interference Through Quantization
  9. Problem Formulation and Proposed Solution
  10. Problem Formulation
  11. Proposed Solution
  12. Analysis of Computational Complexity
  13. Numerical Results
  14. Simulation Parameters
  15. Simulation Results for Monostatic Radar Configuration
  16. ...and 3 more sections

Figures (11)

  • Figure 1: System model of JCAS for monostatic radar.
  • Figure 2: System model of JCAS for bistatic radar.
  • Figure 3: Variation of FI with weight value for $M=100$ for monostatic radar.
  • Figure 4: Trade-off between FI and MI for different number of RIS elements with element spacing of $\lambda/2$ for monostatic radar.
  • Figure 5: Trade-off between FI and MI with $M=64$ and element spacing of $\lambda/4$ for monostatic radar.
  • ...and 6 more figures