# Deep Learning Optimization of Two-State Pinching Antennas Systems

arXiv: https://arxiv.org/abs/2507.06222 · HTML: https://mlsanity.com/paper/2507.06222/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 47 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 30 of 33)

- [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019)
- [Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead](https://mlsanity.com/paper/2004.09352/) (2020)
- [Fluid Antenna Systems](https://mlsanity.com/paper/2005.11561/) (2020)
- [Flexible-Antenna Systems: A Pinching-Antenna Perspective](https://mlsanity.com/paper/2412.02376/) (2024)
- [Movable Antennas for Wireless Communication: Opportunities and Challenges](https://mlsanity.com/paper/2306.02331/) (2023)
- [Modeling and Performance Analysis for Movable Antenna Enabled Wireless Communications](https://mlsanity.com/paper/2210.05325/) (2022)
- [Antenna Activation for NOMA Assisted Pinching-Antenna Systems](https://mlsanity.com/paper/2412.13969/) (2024)
- [Array Gain for Pinching-Antenna Systems (PASS)](https://mlsanity.com/paper/2501.05657/) (2025)
- [Pinching-Antenna Systems (PASS): Architecture Designs, Opportunities, and Outlook](https://mlsanity.com/paper/2501.18409/) (2025)
- [Graph Neural Network Enabled Pinching Antennas](https://mlsanity.com/paper/2502.05447/) (2025)
- [A Tutorial on Movable Antennas for Wireless Networks](https://mlsanity.com/paper/2502.17905/) (2025)
- [Compressed Sensing Based Channel Estimation for Movable Antenna Communications](https://mlsanity.com/paper/2306.04333/) (2023)
- [Movable-Antenna Position Optimization: A Graph-based Approach](https://mlsanity.com/paper/2403.16886/) (2024)
- [Pinching Antennas: Principles, Applications and Challenges](https://mlsanity.com/paper/2501.10753/) (2025)
- [Movable Antennas Meet Intelligent Reflecting Surface: Friends or Foes?](https://mlsanity.com/paper/2501.15880/) (2025)
- [Downlink Beamforming with Pinching-Antenna Assisted MIMO Systems](https://mlsanity.com/paper/2502.01590/) (2025)
- [Performance Analysis of Pinching-Antenna Systems](https://mlsanity.com/paper/2502.06701/) (2025)
- [MIMO-PASS: Uplink and Downlink Transmission via MIMO Pinching-Antenna Systems](https://mlsanity.com/paper/2503.03117/) (2025)
- [Pinching-Antenna Systems (PASS)-enabled Secure Wireless Communications](https://mlsanity.com/paper/2504.13670/) (2025)
- [Fluid Antenna Systems Enabling 6G:Principles, Applications, and Research Directions](https://mlsanity.com/paper/2412.03839/) (2024)
- [Minimum Data Rate Maximization for Uplink Pinching-Antenna Systems](https://mlsanity.com/paper/2412.13892/) (2024)
- [Achieving Network Resilience through Graph Neural Network-enabled Deep Reinforcement Learning](https://mlsanity.com/paper/2501.11074/) (2025)
- [GPASS: Deep Learning for Beamforming in Pinching-Antenna Systems (PASS)](https://mlsanity.com/paper/2502.01438/) (2025)
- [Rate Maximization for Downlink Pinching-Antenna Systems](https://mlsanity.com/paper/2502.12629/) (2025)
- [A Low-Complexity Placement Design of Pinching-Antenna Systems](https://mlsanity.com/paper/2502.14250/) (2025)
- [Toward Generative 6G Simulation: An Experimental Multi-Agent LLM and ns-3 Integration](https://mlsanity.com/paper/2503.13402/) (2025)
- [Rate Maximization for Fluid Antenna System Assisted Semantic Communication](https://mlsanity.com/paper/2506.22943/) (2025)
- [STAR-RIS-Enabled Full-Duplex Integrated Sensing and Communication System](https://mlsanity.com/paper/2410.18767/) (2024)
- [Secrecy Rate Maximization with Artificial Noise for Pinching-Antenna Systems](https://mlsanity.com/paper/2504.10656/) (2025)
- [Cram'er-Rao Bounds for Integrated Sensing and Communications in Pinching-Antenna Systems](https://mlsanity.com/paper/2505.01333/) (2025)

## Abstract (arXiv)

The evolution of wireless communication systems requires flexible, energy-efficient, and cost-effective antenna technologies. Pinching antennas (PAs), which can dynamically control electromagnetic wave propagation through binary activation states, have recently emerged as a promising candidate. In this work, we investigate the problem of optimally selecting a subset of fixed-position PAs to activate in a waveguide, when the aim is to maximize the communication rate at a user terminal. Due to the complex interplay between antenna activation, waveguide-induced phase shifts, and power division, this problem is formulated as a combinatorial fractional 0-1 quadratic program. To efficiently solve this challenging problem, we use neural network architectures of varying complexity to learn activation policies directly from data, leveraging spatial features and signal structure. Furthermore, we incorporate user location uncertainty into our training and evaluation pipeline to simulate realistic deployment conditions. Simulation results demonstrate the effectiveness and robustness of the proposed models.