⚡ electrical engineering

Pinching Antennas-Aided Integrated Sensing and Multicast Communication Systems

This paper proposes a pinching antennas-aided integrated sensing and multicast communication framework that optimizes antenna placement under communication-centric, sensing-centric, and Pareto-optimal criteria for both single and multi-antenna scenarios, demonstrating significant performance gains over fixed-antenna baselines in terms of multicast rate and sensing accuracy.

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

Published 2026-02-19

📖 5 min read🧠 Deep dive

CC BY 4.0

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

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Idea: The "Flexible Fishing Net"

Imagine you are trying to do two things at once in a large, dark room:

Broadcast a message to a group of friends scattered around the room (Multicast Communication).
Pinpoint the exact location of a hidden object (Sensing).

Usually, you'd use a fixed set of speakers and microphones mounted on the walls. But if your friends move, or if the hidden object is behind a pillar, your fixed setup might fail. You'd have to shout louder (more power) or accept poor quality.

This paper introduces a new technology called Pinching-Antenna Systems (PASS).

The Analogy:
Think of PASS not as a wall of speakers, but as a long, flexible garden hose running across the room. Along this hose, you have tiny, detachable "nozzles" (the Pinching Antennas or PAs).

You can slide these nozzles anywhere along the hose.
You can add or remove nozzles as needed.
The hose itself is like a super-efficient wire that carries the signal with almost no loss, even over long distances.

The paper asks: "Where should we place these nozzles to get the best result?"

The Two Goals (The Trade-off)

The researchers found that moving the nozzles to help one goal often hurts the other. It's like trying to stand in the perfect spot to see a movie screen and hear a band playing on stage simultaneously.

Communication Goal (The Party): You want to make sure everyone in the group hears the message clearly. To do this, you want the nozzles close to the people who are far away or in bad spots.
Sensing Goal (The Detective): You want to locate the hidden object as precisely as possible. To do this, you need the nozzles arranged in a specific geometric pattern relative to the object (like triangulation).

The Conflict: The spot that is best for the "Party" might be terrible for the "Detective," and vice versa.

The Three Strategies (The Solutions)

The paper proposes three ways to solve this puzzle, depending on what you care about most:

1. The "Party First" Strategy (Communications-Centric)

Goal: Make sure the message reaches everyone perfectly.
The Solution: The researchers found a mathematical "magic formula" to calculate the exact spot to put the transmitter nozzle. It's like finding the center of a circle that touches all your friends' locations.
The Trick: Even though this strategy focuses on the party, they realized that for the receiving nozzle (the detective's ear), it's best to place it in a mirror image of the target. This symmetry helps the sensing part without hurting the party.

2. The "Detective First" Strategy (Sensing-Centric)

Goal: Find the hidden object with laser precision.
The Solution: They discovered that the best way to locate an object is to place the transmitter and receiver nozzles symmetrically around the object (like a pair of eyes looking at a nose).
The Insight: You don't want the nozzles right on top of the object. Surprisingly, the math shows they should be placed at a specific distance away from the object to get the clearest "picture." It's like how a photographer doesn't stand right on top of the subject to get a good portrait; they step back to get the right angle.

3. The "Perfect Balance" Strategy (Pareto-Optimal)

Goal: Get the best of both worlds.
The Solution: This is the "Goldilocks" zone. The researchers created a method to slide the nozzles back and forth to find the point where you can't improve the sensing without hurting the communication, and vice versa.
The Result: They found that by having more nozzles (more PAs), you can have your cake and eat it too. More nozzles give you enough flexibility to satisfy both the party and the detective simultaneously.

Why This Matters (The "Aha!" Moments)

The paper tested these ideas with computer simulations and found some cool things:

Better than Fixed Walls: Compared to traditional antennas stuck in one place, this "sliding nozzle" system is a massive upgrade. It's like comparing a fixed spotlight to a spotlight on a moving crane.
Crowds are Good: The more people (users) you have in the room, the better this system works for communication. It's like a flexible hose that can stretch to hug a large crowd better than a rigid pipe.
More Nozzles = Better Vision: The more pinching antennas you add, the sharper your "vision" becomes for sensing. It's not just about having more power; it's about having more options for where to put the sensors.

The Bottom Line

This paper is about flexibility. Instead of building a rigid system that struggles when things change, they proposed a system where the antennas can "dance" to the right positions.

Old Way: "Here is a fixed antenna. Good luck."
New Way (PASS): "Here is a long hose with movable nozzles. We will slide them to the perfect spot to make sure your friends hear you and we can find that hidden object."

It turns out that by simply moving the antennas to the right geometric spots, you can get super-fast internet and super-accurate radar without needing to spend a fortune on more power or expensive hardware.

1. Problem Statement

The paper addresses the design of Integrated Sensing and Communications (ISAC) systems utilizing Pinching-Antenna Systems (PASS). While traditional MIMO systems use fixed antennas that limit adaptability, and existing flexible antenna systems (like movable or fluid antennas) suffer from limited movement ranges or double path-loss issues (in the case of RIS), PASS offers a scalable solution using a dielectric waveguide with dynamically attachable/detachable "pinching antennas" (PAs).

The specific challenges addressed are:

Multicast vs. Unicast: Existing PASS research focuses on unicast. However, due to the single RF chain constraint of the waveguide, spatial degrees of freedom (DoF) are limited, making multicast (broadcasting a common message) a more natural and efficient fit.
Sensing Metric: Most studies assume deterministic target parameters and use the standard Cramér-Rao Bound (CRB). In reality, target locations are random with known prior distributions. The paper argues for using the Bayesian Cramér-Rao Bound (BCRB) to better quantify sensing accuracy under uncertainty.
Trade-off Optimization: There is an inherent trade-off between maximizing multicast communication rates and minimizing sensing error (BCRB). The paper seeks to optimize PA placement to navigate this trade-off under three criteria: Communications-Centric (C-C), Sensing-Centric (S-C), and Pareto-optimal designs.

2. Methodology

System Model

Architecture: A Base Station (BS) equipped with a transmit waveguide (Tx-PASS) and a receive waveguide (Rx-PASS) containing $N_t$ and $N_r$ PAs, respectively.
Communication: The BS transmits a common signal to $K$ users. Performance is measured by the Max-Min Fairness (MMF) rate, determined by the user with the worst Signal-to-Noise Ratio (SNR).
Sensing: The system senses a target using reflected echoes. Performance is quantified by the BCRB, which depends on the Bayesian Fisher Information Matrix (BFIM).
BFIM Computation: Since the BCRB involves intractable integrals over the target's prior distribution, the authors propose a Gauss-Hermite Quadrature (GHQ) approach to approximate the BFIM efficiently.

Optimization Frameworks

The paper investigates two scenarios: Single-PA (one transmit, one receive) and Multi-PA (multiple PAs).

A. Single-PA Case (Closed-Form Solutions)

Communications-Centric (C-C): The optimal transmit PA location is derived in closed form. The solution lies in a finite candidate set comprising user coordinates and pairwise intersection points of distance functions.
Sensing-Centric (S-C): The authors prove that for a target with a symmetric prior, the optimal transmit and receive PAs should be placed symmetrically around the target's mean location. A closed-form expression for the optimal displacement from the center is derived.
Pareto-Optimal: A rate-profile formulation is used. By leveraging the symmetry insight from the S-C design, the receive PA is fixed to a symmetric position relative to the transmit PA, reducing the joint optimization to a simple 1D search for the transmit PA.

B. Multi-PA Case (Iterative Algorithms)
The problem becomes highly non-convex. The authors propose an Element-wise Alternating Optimization (AO) framework:

S-C Design: An element-wise AO method sequentially optimizes each PA location to minimize BCRB while satisfying a minimum multicast rate constraint.
C-C Design: An Augmented Lagrangian (AL) framework is introduced. It incorporates the sensing accuracy constraint (BCRB) into the objective function via penalty terms, allowing the element-wise AO to jointly optimize transmit and receive PAs to maximize the multicast rate.
Pareto-Optimal Design: A rate-profile scalarization approach is used, solved via the sequential element-wise AO algorithm to trace the trade-off boundary.

3. Key Contributions

Framework Proposal: First to propose a PASS-aided ISAC framework specifically for multicast communication, utilizing the hardware constraints of the waveguide to its advantage.
Metric Advancement: Introduces the BCRB with a Gauss-Hermite Quadrature method for PASS, moving beyond deterministic CRB assumptions to handle random target priors.
Analytical Insights (Single-PA):
- Derives a closed-form solution for the optimal transmit PA in C-C design.
- Proves and utilizes a symmetric layout for Tx/Rx PAs in S-C design to maximize Fisher Information.
Algorithmic Innovation (Multi-PA): Develops efficient element-wise AO algorithms combined with Augmented Lagrangian methods to solve the non-convex placement problems for C-C and Pareto-optimal designs.
Performance Validation: Extensive numerical results demonstrating that PASS significantly outperforms fixed-array baselines.

4. Numerical Results

Performance Gain: PASS significantly outperforms fixed-location antenna systems (both analog and digital beamforming) in both multicast rate and sensing accuracy (lower BCRB).
User Density: The multicast gain of PASS becomes more pronounced as user density increases, validating its suitability for multicast scenarios.
Sensing Accuracy: Sensing accuracy improves with the number of deployed PAs. The S-C design consistently achieves the lowest BCRB.
Trade-off: The Pareto frontier analysis shows that increasing the number of PAs expands the achievable trade-off region, mitigating the conflict between communication rate and sensing accuracy.
Robustness: The C-C design, even without explicit sensing optimization, achieves better sensing performance than random PA layouts due to the structural symmetry of the receive PA placement.

5. Significance

This work is significant because it bridges the gap between theoretical ISAC potential and practical hardware implementation using Pinching Antennas.

Hardware Efficiency: It demonstrates that a single RF chain with a flexible waveguide can outperform complex multi-RF chain fixed arrays, offering a low-cost, high-performance solution.
Geometric Optimization: It shifts the paradigm from "power scaling" to "geometric reconfiguration," showing that optimizing the physical placement of antennas is a more effective lever for improving ISAC performance than simply increasing transmit power.
Multicast Focus: By addressing the multicast scenario, it opens new avenues for applying flexible antenna systems in broadcast and group communication services (e.g., public safety, vehicular networks) where fixed arrays struggle with interference and coverage.

In conclusion, the paper establishes that PASS is a superior architecture for ISAC, capable of dynamically adapting to user and target distributions to achieve optimal sensing and communication performance through intelligent antenna placement.