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Rate Maximization for Multi-Waveguide PASS: A Hierarchical User Scheduling and Joint Optimization Framework

This paper investigates sum rate maximization in multi-waveguide pinching-antenna systems by proposing a hierarchical user scheduling algorithm to mitigate path loss and interference, alongside a joint optimization framework for power allocation and antenna positioning that explicitly accounts for in-waveguide propagation loss and coupling effects.

Original authors: Guangyu Li, Xin Sun, Tianwei Hou, Anna Li, Yuanwei Liu, Arumugam Nallanathan

Published 2026-05-29

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Guangyu Li, Xin Sun, Tianwei Hou, Anna Li, Yuanwei Liu, Arumugam Nallanathan

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

Imagine you are trying to host a massive outdoor party where you need to broadcast music to 9 different groups of people scattered across a large field. In the old days, you would use a few giant, fixed speakers (traditional antennas). But these speakers are rigid; they can't move, and the sound gets muddy or quiet if the people are far away or if the wind (interference) gets in the way.

This paper introduces a new, high-tech way to handle this party using something called Pinching-Antenna Systems (PASS).

The Setup: The "Magic Hose"

Instead of fixed speakers, imagine you have three long, flexible garden hoses (dielectric waveguides) stretched out above the crowd. Along these hoses, you have tiny, magical "pinch points" (the antennas).

How it works: You can turn these pinch points on or off, and you can slide them to different spots along the hose. When you "pinch" the hose at a specific spot, it releases a burst of sound (signal) right there.
The Goal: By moving these pinch points around, you can aim the sound perfectly at the people who need it, making the music loud and clear for everyone.

The Problem: The "Leaky Hose" and the "Bump"

The researchers realized that previous theories treated these hoses as perfect. But in the real world, two things go wrong:

The Leaky Hose (Propagation Loss): As the signal travels down the hose, it gets weaker, especially if the frequency is high (like a high-pitched whistle). It's like water leaking out of a long, porous pipe before it reaches the end.
The Bump (Coupling Effect): When you have multiple pinch points close together on the same hose, they start to "talk" to each other. If you squeeze one too hard, it messes up the squeeze of its neighbor. This creates interference and distorts the signal.

The paper builds a new mathematical model that accounts for both the leaking and the bumping. It's like realizing your garden hose isn't perfect and calculating exactly how much water you'll lose so you can plan better.

The Solution: The "Smart Party Planner"

To get the best music for everyone, the authors propose a two-step "Smart Party Planner" (a Hierarchical User Scheduling algorithm):

Step 1: Grouping (Pairing): First, they look at where the guests are standing. They assign each guest to the hose that is physically closest to them. This is like telling a guest, "Go stand near the hose right next to you so you don't have to shout." This reduces the distance the sound has to travel, saving energy.
Step 2: Timing (Scheduling): Next, they decide who gets to listen at the same time. They make sure that if three people are listening at once, they are far apart from each other. If you put three people standing right next to each other in the same time slot, their signals would crash into each other (interference). By spacing them out, the "noise" between them is reduced.

The Optimization: The "Sliding Puzzle"

Once the groups are set, the system does a final fine-tuning:

Sliding the Pinches: It calculates the exact best spot to slide each pinch point along the hose. It's like sliding a slider on a soundboard to find the "sweet spot" where the volume is perfect.
Sharing the Power: It decides how much "power" (volume) to give to each hose. Sometimes, it's better to give a little extra power to the person with the worst connection to make sure they can hear, rather than blasting the person who is already close.

What They Found (The Results)

The researchers ran simulations (computer tests) to see how well this new system works compared to old methods:

Realism Matters: If you ignore the "leaky hose" and the "bumping" effects, your plan will fail. The paper shows that at high frequencies, ignoring these physical realities makes the system perform much worse.
Better than Random: Their "Smart Party Planner" (HUS) works much better than just randomly assigning people to hoses or timeslots.
The Surprise: In a multi-hose system, the "bumping" effect (coupling) actually forces the pinch points to be spaced out. Surprisingly, this spacing helps the system redistribute power in a way that actually improves the total volume for the whole party, rather than hurting it.
Efficiency: Their method of adjusting the power and position gets much better results than just turning up the volume on all speakers (Maximum Ratio Transmission).

The Bottom Line

This paper provides a practical guide for building these new, flexible antenna systems. It proves that by understanding the physical quirks of the "hoses" (loss and coupling) and using a smart, two-step scheduling system, we can deliver much faster and more reliable wireless connections to many users at once, without needing to blast massive amounts of power.

Technical Summary: Rate Maximization for Multi-Waveguide PASS

Problem Statement
This study investigates the sum rate maximization problem in multi-waveguide Pinching-Antenna Systems (PASS). While PASS offers a flexible antenna architecture capable of dynamically reconfiguring wireless channels via dielectric particles (pinching antennas or PAs) along waveguides, existing literature often relies on idealized models. Specifically, current research lacks a unified model that jointly characterizes in-waveguide propagation loss and waveguide-PA coupling effects. Furthermore, the joint optimization of PA positioning and power allocation under user minimum rate constraints, combined with systematic user scheduling in multi-waveguide environments, remains largely unexplored.

Methodology
To address these gaps, the authors develop a comprehensive framework comprising physical modeling, user scheduling, and joint optimization:

Unified Physical Model:
- Propagation Loss: Based on electromagnetic (EM) field theory, the authors model signal attenuation and phase shift within dissipative dielectric waveguides. The propagation constant is treated as a complex number ( $\gamma_g = \alpha_g + j\beta_g$ ), where the loss component ( $\alpha_g$ ) depends on the dielectric loss tangent and frequency.
- Coupling Effect: Using coupled-mode theory, the interaction between the waveguide and the PAs (modeled as open-ended directional couplers) is quantified. The model derives coupling coefficients that determine how much energy is transferred from the waveguide to the PA and how the residual field propagates. This results in a discrete positioning constraint for PAs, as their physical size and coupling length prevent arbitrary placement.
Hierarchical User Scheduling (HUS) Algorithm:
- Waveguide-User Pairing: A convex optimization problem is formulated to minimize the sum of squared distances between users and waveguides. This step aims to reduce large-scale path loss by associating users with geometrically proximate waveguides.
- In-Slot User Selection: Within the Time Division Multiple Access (TDMA) framework, users are scheduled in time slots to maximize spatial separation. This strategy increases the propagation distance of interference signals, thereby mitigating inter-waveguide interference and improving the Signal-to-Interference-plus-Noise Ratio (SINR).
Joint Optimization Framework:
- An Alternating Optimization (AO) approach is employed to maximize the system sum rate subject to minimum rate constraints and total power budgets.
- PA Positioning: Optimized via a one-dimensional grid search over feasible intervals defined by adjacent PAs.
- Power Allocation: The non-convex power allocation problem is transformed using Lagrangian duality and Fractional Programming (FP). The authors introduce auxiliary variables to reformulate the logarithmic sum rate into a tractable fractional form, which is then solved iteratively using Successive Convex Approximation (SCA) with first-order Taylor expansions.

Key Contributions

Unified Physical Modeling: The paper presents the first multi-waveguide PASS model that simultaneously incorporates in-waveguide propagation loss and waveguide-PA coupling effects, derived from EM field and coupled-mode theories.
HUS Algorithm: A low-complexity hierarchical scheduling algorithm is proposed to reduce path loss and inter-user interference, outperforming random pairing strategies.
Joint Optimization Framework: A novel framework integrating PA positioning and power allocation is developed. It utilizes one-dimensional search for positioning and SCA-based convex optimization for power allocation, ensuring convergence to a stationary point.
Physical Insights: The study provides numerical evidence on how propagation loss and coupling effects influence system performance, particularly highlighting the trade-offs in high-frequency applications.

Numerical Results
Simulations conducted with polytetrafluoroethylene waveguides reveal several key findings:

Impact of Loss and Coupling: In-waveguide propagation loss significantly degrades performance, with the gap between idealized and lossy models widening at higher carrier frequencies (e.g., 28 GHz). Coupling effects necessitate discrete PA positioning, which introduces additional constraints.
Counterintuitive Gain: In multi-waveguide scenarios, the system accounting for coupling effects (AWS) occasionally outperforms the dissipative waveguide scheme (DWS) without coupling constraints. This is attributed to "power redistribution gain," where discrete positioning forces a reallocation of power to non-core users, improving the overall sum rate when core users are already saturated.
Algorithm Performance: The proposed HUS algorithm and joint optimization framework consistently outperform benchmarks such as Maximum Ratio Transmission (MRT) and random pairing. The proposed power allocation scheme shows marked improvement over MRT, particularly as transmit power increases.
Convergence: The proposed algorithms demonstrate fast convergence (typically within five iterations) and robustness against different initialization strategies.

Significance and Claims
The paper claims to provide a practical and efficient solution for the physical modeling, user scheduling, and joint optimization of multi-waveguide PASS. The authors emphasize that accurate electromagnetic modeling, including both propagation loss and coupling effects, is essential for reliable performance evaluation. They assert that their proposed framework effectively exploits temporal and spatial resources to maximize sum rate while maintaining user fairness. The study concludes that optimized resource allocation and scheduling offer greater benefits than simply increasing transmit power, providing valuable guidance for the design of next-generation flexible antenna systems. Future work is suggested to extend this framework to dynamic scenarios involving user mobility and coordinated multi-point transmission.