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The ISAC systems aided by MIMO, RIS and with Beamforming Techniques

Rafael Augusto Pedriali, Isadora Martines Ferreira, Jose Carlos Marinello, Taufik Abrao

TL;DR

The paper addresses joint sensing and communication in wireless networks by integrating a BS, RIS, and beamforming to mitigate LoS/NLoS challenges and enhance spectral efficiency. It proposes a time-multiplexed ISAC framework with static RIS configurations, enabling centralized processing and reduced complexity. Localization is achieved in 3D via trilateration using ToA and AoA, supported by a linearized optimization approach that yields closed-form position estimates. Through complexity analyses and Monte Carlo simulations, the study demonstrates substantial gains in sensing range, energy efficiency, and reliability, suggesting practical potential for dense urban deployments and future multi-user extensions.

Abstract

This paper explores the integration of communication and sensing in modern wireless systems through the configuration of BS and RIS antenna elements. By leveraging time multiplexing for both communication and sensing, the proposed system optimizes spectral efficiency and operational performance. The use of static RIS configurations tailored to specific environments eliminates the need for dynamic reconfigurations, enhancing system agility, reducing processing complexity, and improving sensing accuracy. The system incorporates trilateration, angle of arrival, and time of arrival techniques to enable precise user localization by combining signals reflected along multiple paths. This method helps choose the best connections and lowers sensing costs while preventing interference with communication data, highlighting the need to bring together new technologies like passive and adaptive beamforming in one system.

The ISAC systems aided by MIMO, RIS and with Beamforming Techniques

TL;DR

The paper addresses joint sensing and communication in wireless networks by integrating a BS, RIS, and beamforming to mitigate LoS/NLoS challenges and enhance spectral efficiency. It proposes a time-multiplexed ISAC framework with static RIS configurations, enabling centralized processing and reduced complexity. Localization is achieved in 3D via trilateration using ToA and AoA, supported by a linearized optimization approach that yields closed-form position estimates. Through complexity analyses and Monte Carlo simulations, the study demonstrates substantial gains in sensing range, energy efficiency, and reliability, suggesting practical potential for dense urban deployments and future multi-user extensions.

Abstract

This paper explores the integration of communication and sensing in modern wireless systems through the configuration of BS and RIS antenna elements. By leveraging time multiplexing for both communication and sensing, the proposed system optimizes spectral efficiency and operational performance. The use of static RIS configurations tailored to specific environments eliminates the need for dynamic reconfigurations, enhancing system agility, reducing processing complexity, and improving sensing accuracy. The system incorporates trilateration, angle of arrival, and time of arrival techniques to enable precise user localization by combining signals reflected along multiple paths. This method helps choose the best connections and lowers sensing costs while preventing interference with communication data, highlighting the need to bring together new technologies like passive and adaptive beamforming in one system.
Paper Structure (25 sections, 42 equations, 7 figures, 2 tables)

This paper contains 25 sections, 42 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Channel Model
  4. Rice fading channel
  5. Adaptive beamforming applied to the MIMO system
  6. RIS
  7. Beanforming passivo
  8. Three-dimensional Localization Modelling
  9. Sensing system model
  10. Sensing Operation Mode
  11. Sensing equations
  12. Optimization Problem
  13. ToA and AoA techniques
  14. Integration of Communication and Sensing
  15. ISAC Complexity Analyses
  16. ...and 10 more sections

Figures (7)

  • Figure 1: ISAC system model composed by one BS and 2 RIS.
  • Figure 2: Proposed ISAC Frame Structure with Time Allocation.
  • Figure 3: Path identification based on AoA technique.
  • Figure 4: ISAC Sensing, a) BS emission; b) UE reflection; c) RIS-Assisted redirection.
  • Figure 5: Illustration of the ISAC system operating in downlink mode with the BS using 31 antennas.
  • ...and 2 more figures