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arxiveess.SY2026-07-08

A Unified Fully Reconfigurable Architecture for Wireless Powered Communication Networks

Bingxin Zhang, Yizhe Zhao, Kun Yang

Wireless powered communication networks (WPCNs) are a key enabler for sustainable Internet of Things (IoT) systems, yet their practical performance is constrained by inefficient wireless energy transfer, limited spatial adaptability, and fragile uplink connectivity in blockage-prone and dynamic environments. Emerging reconfigurable antenna technologies, including pinching antenna systems (PASSs), fluid antenna systems (FASs), movable antennas (MAs), and reconfigurable intelligent surfaces (RISs), provide new opportunities to overcome these limitations, but have mostly been studied separately. In this article, we propose a unified architecture for fully reconfigurable WPCNs by jointly integrating PASS-enabled power beacons, FAS-based IoT devices, MA-assisted base stations, and RIS-aided propagation environments. The proposed framework enables end-to-end reconfigurability across downlink energy transfer, device-side spatial adaptation, base-station reception, and uplink information transmission. We further discuss the integration motivation, system architecture, design and optimization framework, illustrative performance evaluation, implementation tradeoffs, and major practical challenges. This article provides a new perspective for designing next-generation fully reconfigurable WPCNs.

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arxivcs.ROeess.SY2026-07-24

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arxivcs.NIcs.MAeess.SY2026-07-24

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This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Awa…

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arxiveess.SY2026-07-24

Physics-Informed Neural Network for Modeling the Dynamic Behavior of Grid-Forming Converters

Hussein Jaffal, Arianna Fois, Sarra Bouchkati, Amirali Mahjoob, Andreas Ulbig

This paper investigates physics-informed neural networks for modeling the full dynamic behavior of droop-controlled grid-forming converters. The approach is trained on synthetic data generated via numerical solvers and benchmarked against both traditional integration methods and…

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arxiveess.SY2026-07-24

StateFormer: A Multivariate Transformer for Learning History-Dependent Battery State Dynamics and Long-Horizon Health Forecasting

Zhe Bai, Stephen Harris

This paper introduces a novel multivariate Transformer \emph{StateFormer} that forecasts degradation dynamics of large-scale battery systems. The model learns across time scales, from short-term thermal fluctuations to long-term aging trajectories, enabling accurate prediction of…

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