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Integrated Sensing and Communication in IRS-assisted High-Mobility Systems: Design, Analysis and Optimization

Xingyu Peng, Qin Tao, Xiaoling Hu, Richeng Jin, Chongwen Huang, Xiaoming Chen

TL;DR

This paper designs a low-complexity ratio-based sensing algorithm for estimating the velocity of mobile user and analyzes the performance of sensing and communication in terms of achievable mean square error (MSE) and achievable rate, respectively, and reveals the impact of key parameters.

Abstract

In this paper, we investigate integrated sensing and communication (ISAC) in high-mobility systems with the aid of an intelligent reflecting surface (IRS). To exploit the benefits of Delay-Doppler (DD) spread caused by high mobility, orthogonal time frequency space (OTFS)-based frame structure and transmission framework are proposed. {In such a framework,} we first design a low-complexity ratio-based sensing algorithm for estimating the velocity of mobile user. Then, we analyze the performance of sensing and communication in terms of achievable mean square error (MSE) and achievable rate, respectively, and reveal the impact of key parameters. Next, with the derived performance expressions, we jointly optimize the phase shift matrix of IRS and the receive combining vector at the base station (BS) to improve the overall performance of integrated sensing and communication. Finally, extensive simulation results confirm the effectiveness of the proposed algorithms in high-mobility systems.

Integrated Sensing and Communication in IRS-assisted High-Mobility Systems: Design, Analysis and Optimization

TL;DR

This paper designs a low-complexity ratio-based sensing algorithm for estimating the velocity of mobile user and analyzes the performance of sensing and communication in terms of achievable mean square error (MSE) and achievable rate, respectively, and reveals the impact of key parameters.

Abstract

In this paper, we investigate integrated sensing and communication (ISAC) in high-mobility systems with the aid of an intelligent reflecting surface (IRS). To exploit the benefits of Delay-Doppler (DD) spread caused by high mobility, orthogonal time frequency space (OTFS)-based frame structure and transmission framework are proposed. {In such a framework,} we first design a low-complexity ratio-based sensing algorithm for estimating the velocity of mobile user. Then, we analyze the performance of sensing and communication in terms of achievable mean square error (MSE) and achievable rate, respectively, and reveal the impact of key parameters. Next, with the derived performance expressions, we jointly optimize the phase shift matrix of IRS and the receive combining vector at the base station (BS) to improve the overall performance of integrated sensing and communication. Finally, extensive simulation results confirm the effectiveness of the proposed algorithms in high-mobility systems.
Paper Structure (25 sections, 3 theorems, 79 equations, 9 figures, 1 table, 2 algorithms)

This paper contains 25 sections, 3 theorems, 79 equations, 9 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. Related Work
  4. Contributions
  5. System Model
  6. Time Domain Channel Model
  7. OTFS-based Transmission Framework
  8. Symbol Arrangement in OTFS Frame
  9. Design of Velocity Sensing Algorithm
  10. Ratio-based Sensing Algorithm
  11. Performance of the Proposed Sensing Algorithm
  12. Optimization of Integrated Sensing and Communication
  13. Problem Formulation
  14. Algorithm Design
  15. Algorithm Analysis
  16. ...and 10 more sections

Key Result

Theorem 1

For the ideal case that the received signal is noiseless, the value of $\nu_1^{{\rm{UI}}}$ can be estimated by the ratio of the PP and SPP tao1 , i.e., where $m$ is an integer making ${\hat{\nu}_1}\in\left[ -\frac{1}{2NT_s},0\right)$, if $\mathbf{Z}_{k_2,l}'>\mathbf{Z}_{k_3,l}'$, and ${\hat{\nu}_1}\in\left( 0,\frac{1}{2NT_s}\right]$, if $\mathbf{Z}_{k_2,l}'<\mathbf{Z}_{k_3,l}'$, and Proof: See

Figures (9)

  • Figure 1: System model: An IRS-assisted high-mobility system.
  • Figure 2: The OTFS-based transmission framework.
  • Figure 3: The arrangement of pilot and data symbols in the OTFS frame.
  • Figure 4: Effective sensing probability of the proposed ratio-based sensing algorithm.
  • Figure 5: The achievable MSE of the proposed ratio-based sensing algorithm.
  • ...and 4 more figures

Theorems & Definitions (6)

  • Theorem 1
  • Remark 1
  • Theorem 2
  • Remark 2
  • Corollary 1
  • Remark 3