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Pinching Antennas-Aided Integrated Sensing and Multicast Communication Systems

Shan Shan, Chongjun Ouyang, Xiaohang Yang, Yong Li, Zhiqin Wang, Yuanwei Liu

TL;DR

Numerical results show that PASS substantially outperforms fixed-antenna baselines in both multicast rate and sensing accuracy; ii) the multicasting gain becomes more pronounced as the user density increases; and iii) the sensing accuracy improves with the number of deployed PAs.

Abstract

A pinching antennas (PAs)-aided integrated sensing and multicast communication framework is proposed. In this framework, the communication performance is measured by the multicast rate considering max-min fairness. Moreover, the sensing performance is quantified by the Bayesian Cramér-Rao bound (BCRB), where a Gauss-Hermite quadrature-based approach is proposed to compute the Bayesian Fisher information matrix. Based on these metrics, PA placement is optimized under three criteria: communications-centric (C-C), sensing-centric (S-C), and Pareto-optimal designs. These designs are investigated in two scenarios: the single-PA case and the multi-PA case. 1) For the single-PA case, a closed-form solution is derived for the location of the C-C transmit PA, while the S-C design yields optimal transmit and receive PA placements that are symmetric about the target location. Leveraging this geometric insight, the Pareto-optimal design is solved by enforcing this PA placement symmetry, thereby reducing the joint transmit and receive PA placement to the transmit PA optimization. 2) For the general multi-PA case, the PA placements constitute a highly non-convex optimization problem. To solve this, an element-wise alternating optimization-based method is proposed to sequentially optimize all PA placements for the S-C design, and is further incorporated into an augmented Lagrangian (AL) framework and a rate-profile formulation to solve the C-C and Pareto-optimal design problems, respectively. Numerical results show that: i) PASS substantially outperforms fixed-antenna baselines in both multicast rate and sensing accuracy; ii) the multicasting gain becomes more pronounced as the user density increases; and iii) the sensing accuracy improves with the number of deployed PAs.

Pinching Antennas-Aided Integrated Sensing and Multicast Communication Systems

TL;DR

Numerical results show that PASS substantially outperforms fixed-antenna baselines in both multicast rate and sensing accuracy; ii) the multicasting gain becomes more pronounced as the user density increases; and iii) the sensing accuracy improves with the number of deployed PAs.

Abstract

A pinching antennas (PAs)-aided integrated sensing and multicast communication framework is proposed. In this framework, the communication performance is measured by the multicast rate considering max-min fairness. Moreover, the sensing performance is quantified by the Bayesian Cramér-Rao bound (BCRB), where a Gauss-Hermite quadrature-based approach is proposed to compute the Bayesian Fisher information matrix. Based on these metrics, PA placement is optimized under three criteria: communications-centric (C-C), sensing-centric (S-C), and Pareto-optimal designs. These designs are investigated in two scenarios: the single-PA case and the multi-PA case. 1) For the single-PA case, a closed-form solution is derived for the location of the C-C transmit PA, while the S-C design yields optimal transmit and receive PA placements that are symmetric about the target location. Leveraging this geometric insight, the Pareto-optimal design is solved by enforcing this PA placement symmetry, thereby reducing the joint transmit and receive PA placement to the transmit PA optimization. 2) For the general multi-PA case, the PA placements constitute a highly non-convex optimization problem. To solve this, an element-wise alternating optimization-based method is proposed to sequentially optimize all PA placements for the S-C design, and is further incorporated into an augmented Lagrangian (AL) framework and a rate-profile formulation to solve the C-C and Pareto-optimal design problems, respectively. Numerical results show that: i) PASS substantially outperforms fixed-antenna baselines in both multicast rate and sensing accuracy; ii) the multicasting gain becomes more pronounced as the user density increases; and iii) the sensing accuracy improves with the number of deployed PAs.
Paper Structure (38 sections, 2 theorems, 87 equations, 9 figures, 3 algorithms)

This paper contains 38 sections, 2 theorems, 87 equations, 9 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Motivations and Contributions
  4. System Model
  5. Multicast Communication Performance Metric
  6. Sensing Performance Metric
  7. PFIM Derivation
  8. OFIM Derivation
  9. Single-PA Case
  10. Communications-Centric Design
  11. Sensing-Centric Design
  12. Pareto-Optimal Design
  13. Sensing-Driven Rx-PA Placement
  14. Tx-PA Placement Optimization
  15. Multi-PA Case
  16. ...and 23 more sections

Key Result

Proposition 1

For any given target $x$-coordinate $u^x$, under the symmetric setup $u^y=(y_{\rm t}+y_{\rm r})/2$, the conditional observation Fisher information $F_{xx}(u^x,x_{\rm t},x_{\rm r})$ is maximized when the Tx-PA and Rx-PA are placed symmetrically around the target along the $x$-axis, i.e., $|u^x-x_{\rm

Figures (9)

  • Figure 1: Illustration of PA-aided integrated sensing and multicast transmission.
  • Figure 2: Multicast rate versus the side length $D_{\rm x}$ for single-PA case.
  • Figure 3: BCRB versus the transmit power for single-PA case.
  • Figure 4: The optimal Tx/Rx-PA placement for maximizing the OFIM (dB) under the proposed closed-form solution and the exhaustive search benchmark.
  • Figure 5: Patero-optimal design for single-PA case.
  • ...and 4 more figures

Theorems & Definitions (3)

  • Proposition 1
  • Proposition 2
  • Remark 1