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A Survey on Pinching-Antenna Systems: Optimization Techniques, Intelligent Designs, and Multifunctional Applications
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DOI：10.1109/comst.2026.3710938.png)
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Abstract
En 中文
Mobile communication systems are undergoing a hardware-driven technological evolution, aiming to enhance the controllability and programmability of wireless propagation. Recently, pinching antennas (PAs) have emerged as an innovative flexible-antenna technology, capable of dynamically adjusting PA positions along waveguides to construct line-of-sight (LoS) links and mitigate large-scale path loss and LoS blockage. In parallel, integrating artificial intelligence (AI) into wireless system designs and enabling multifunctional wireless services have become an inevitable trend. PA systems particularly necessitate deep learning (DL)-enabled approaches due to their inherent optimization complexity, while PAs’ ability to customize wireless channel characteristics supports the deployment of multifunctional services. This article surveys the state-of-the-art studies to provide a comprehensive overview PAs. We first outline the fundamental principles of PAs, classify existing PA systems based on PA configurations, implementation schemes, and channel models, and summarize the key analysis results. Subsequently, we present the reviews of classic optimization techniques for PA-enabled information transmission, non-orthogonal multiple access (NOMA) schemes, and beyond fifth-generation (B5G) technologies. Next, we introduce DL-enabled PA designs, covering unsupervised learning, deep reinforcement learning (DRL), and convex-aided DL frameworks. We further summarize PA-enabled multifunctional service designs tailored to scenarios including information security, wireless powered networks, and integrated sensing and communication (ISAC). Finally, we highlight the critical challenges, open issues, and promising future research directions for PAs.
Keywords:
PA
optimization techniques
DL
DRL
ISAC
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