Deep Learning Optimization of Two-State Pinching Antennas Systems

• URL: http://arxiv.org/abs/2507.06222v1
• Date: Tue, 08 Jul 2025 17:55:54 GMT
• Title: Deep Learning Optimization of Two-State Pinching Antennas Systems
• Authors: Odysseas G. Karagiannidis, Victoria E. Galanopoulou, Panagiotis D. Diamantoulakis, Zhiguo Ding, Octavia Dobre,
• Abstract summary: Pinching antennas (PAs) can dynamically control electromagnetic wave propagation through binary activation states.<n>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.
• Score: 48.70043547158868
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• AMR-Transformer: Enabling Efficient Long-range Interaction for Complex Neural Fluid Simulation [33.63726923336252]
  We propose AMR-Transformer, an efficient and accurate neural CFD-solving pipeline.<n>It integrates a novel adaptive mesh refinement scheme with a Navier-Stokes constraint-aware fast pruning module.<n>Our approach achieves an order-of-magnitude improvement in accuracy over baseline models.
  arXiv  Detail & Related papers  (2025-03-13T11:16:42Z)
• Joint Transmit and Pinching Beamforming for Pinching Antenna Systems (PASS): Optimization-Based or Learning-Based? [89.05848771674773]
  A novel antenna system ()-enabled downlink multi-user multiple-input single-output (MISO) framework is proposed.<n>It consists of multiple waveguides, which equip numerous low-cost antennas, named (PAs)<n>The positions of PAs can be reconfigured to both spanning large-scale path and space.
  arXiv  Detail & Related papers  (2025-02-12T18:54:10Z)
• Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning [59.660724802286865]
  Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications.<n>This work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users.
  arXiv  Detail & Related papers  (2025-02-09T09:15:47Z)
• DRL-based Dolph-Tschebyscheff Beamforming in Downlink Transmission for Mobile Users [52.9870460238443]
  We propose a deep reinforcement learning-based blind beamforming technique using a learnable Dolph-Tschebyscheff antenna array.<n>Our simulation results show that the proposed method can support data rates very close to the best possible values.
  arXiv  Detail & Related papers  (2025-02-03T11:50:43Z)
• Joint Adaptive OFDM and Reinforcement Learning Design for Autonomous Vehicles: Leveraging Age of Updates [2.607046313483251]
  Millimeter wave (mmWave)-based frequency-division multiplexing (OFDM) stands out as a suitable alternative for high-resolution sensing and high-speed data transmission.<n>In this work, we consider an autonomous vehicle network where an AV utilizes its queue state information (QSI) and channel state information (CSI) in conjunction with reinforcement learning techniques to manage communication and sensing.
  arXiv  Detail & Related papers  (2024-12-24T15:32:58Z)
• Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning [60.94661435297309]
  The scale of mobile networks makes it challenging to optimize antenna parameters using manual intervention or hand-engineered strategies. We propose a new multi-agent reinforcement learning algorithm to optimize mobile network configurations globally. We empirically demonstrate the performance of the algorithm on an antenna tilt tuning problem and a joint tilt and power control problem in a simulated environment.
  arXiv  Detail & Related papers  (2023-01-20T17:06:34Z)
• Learning OFDM Waveforms with PAPR and ACLR Constraints [15.423422040627331]
  We propose a learning-based method to design OFDM-based waveforms that satisfy selected constraints while maximizing an achievable information rate. We show that the end-to-end system is able to satisfy target PAPR and ACLR constraints and allows significant throughput gains.
  arXiv  Detail & Related papers  (2021-10-21T08:58:59Z)
• Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots [58.980293789967575]
  A communication enabled indoor intelligent robots (IRs) service framework is proposed. Lego modeling method is proposed, which can deterministically describe the indoor layout and channel state. The investigated radio map is invoked as a virtual environment to train the reinforcement learning agent.
  arXiv  Detail & Related papers  (2020-11-23T21:45:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.