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IRS Aided Millimeter-Wave Sensing and Communication: Beam Scanning, Beam Splitting, and Performance Analysis

Renwang Li, Xiaodan Shao, Shu Sun, Meixia Tao, Rui Zhang

TL;DR

This work addresses joint sensing and communication at mmWave frequencies by leveraging a semi-passive IRS to create virtual LoS paths for both data transmission and target angle estimation. It introduces a practical two-phase ISAC protocol that uses beam scanning during Phase I for initial beam and angle estimation and leverages data transmission in Phase II for enhanced sensing through either single-beam or beam-splitting strategies. The authors derive the achievable rate and CRB/MSE expressions, and show how system resources (REs/SEs, codebook size, and beam scanning duration) influence the trade-off between communication performance and sensing accuracy. Simulations corroborate the analysis, demonstrating the viability of the proposed approach and providing design insights for deploying IRS-aided ISAC in practical mmWave networks.

Abstract

Integrated sensing and communication (ISAC) has attracted growing interests for enabling the future 6G wireless networks, due to its capability of sharing spectrum and hardware resources between communication and sensing systems. However, existing works on ISAC usually need to modify the communication protocol to cater for the new sensing performance requirement, which may be difficult to implement in practice. In this paper, we study a new intelligent reflecting surface (IRS) aided millimeter-wave (mmWave) ISAC system by exploiting the distinct beam scanning operation in mmWave communications to achieve efficient sensing at the same time. First, we propose a two-phase ISAC protocol aided by a semi-passive IRS, consisting of beam scanning and data transmission. Specifically, in the beam scanning phase, the IRS finds the optimal beam for reflecting signals from the base station to a communication user via its passive elements. Meanwhile, the IRS directly estimates the angle of a nearby target based on echo signals from the target using its equipped active sensing element. Then, in the data transmission phase, the sensing accuracy is further improved by leveraging the data signals via possible IRS beam splitting. Next, we derive the achievable rate of the communication user as well as the Cramér-Rao bound and the approximate mean square error of the target angle estimation Finally, extensive simulation results are provided to verify our analysis as well as the effectiveness of the proposed scheme.

IRS Aided Millimeter-Wave Sensing and Communication: Beam Scanning, Beam Splitting, and Performance Analysis

TL;DR

This work addresses joint sensing and communication at mmWave frequencies by leveraging a semi-passive IRS to create virtual LoS paths for both data transmission and target angle estimation. It introduces a practical two-phase ISAC protocol that uses beam scanning during Phase I for initial beam and angle estimation and leverages data transmission in Phase II for enhanced sensing through either single-beam or beam-splitting strategies. The authors derive the achievable rate and CRB/MSE expressions, and show how system resources (REs/SEs, codebook size, and beam scanning duration) influence the trade-off between communication performance and sensing accuracy. Simulations corroborate the analysis, demonstrating the viability of the proposed approach and providing design insights for deploying IRS-aided ISAC in practical mmWave networks.

Abstract

Integrated sensing and communication (ISAC) has attracted growing interests for enabling the future 6G wireless networks, due to its capability of sharing spectrum and hardware resources between communication and sensing systems. However, existing works on ISAC usually need to modify the communication protocol to cater for the new sensing performance requirement, which may be difficult to implement in practice. In this paper, we study a new intelligent reflecting surface (IRS) aided millimeter-wave (mmWave) ISAC system by exploiting the distinct beam scanning operation in mmWave communications to achieve efficient sensing at the same time. First, we propose a two-phase ISAC protocol aided by a semi-passive IRS, consisting of beam scanning and data transmission. Specifically, in the beam scanning phase, the IRS finds the optimal beam for reflecting signals from the base station to a communication user via its passive elements. Meanwhile, the IRS directly estimates the angle of a nearby target based on echo signals from the target using its equipped active sensing element. Then, in the data transmission phase, the sensing accuracy is further improved by leveraging the data signals via possible IRS beam splitting. Next, we derive the achievable rate of the communication user as well as the Cramér-Rao bound and the approximate mean square error of the target angle estimation Finally, extensive simulation results are provided to verify our analysis as well as the effectiveness of the proposed scheme.
Paper Structure (18 sections, 5 theorems, 64 equations, 14 figures, 1 table)

This paper contains 18 sections, 5 theorems, 64 equations, 14 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model and Protocol Design
  3. System Model
  4. Channel Model
  5. Proposed Protocol for ISAC
  6. Performance Analysis for Initial Beam Scanning
  7. Achievable Rate of Communication User
  8. CRB and MSE for Initial Sensing in Phase I
  9. Enhanced Sensing during Data Transmission
  10. Single Beam Based Sensing
  11. Beam Splitting Based Sensing
  12. Simulation Results
  13. Communication and Sensing Trade-off in Phase I
  14. Communication and Sensing Trade-off in Phase II
  15. Conclusion
  16. ...and 3 more sections

Key Result

Theorem 1

The angle estimated via the MLE is given by which can be solved by exhaustive search over $\left[-1, 1 \right]$.

Figures (14)

  • Figure 1: System model of IRS-aided ISAC.
  • Figure 2: IRS-aided mmWave ISAC protocol.
  • Figure 3: MSE versus SNR.
  • Figure 4: IRS beamforming gain $G_\text{IRS}$ versus $\delta_{UT}$ when $M=60$.
  • Figure 5: MSE versus transmit power in Phase I when $M=64$ and $M_s=12$.
  • ...and 9 more figures

Theorems & Definitions (10)

  • Theorem 1
  • proof
  • Theorem 2
  • proof
  • Theorem 3
  • proof
  • Corollary 1
  • proof
  • Theorem 4
  • proof