Energy Harvesting in Wireless Networks

Últimos artículos

• Constraint-Aware Aggregation for Federated Reinforcement Learning in Microgrid Energy Coordination

  Usman Haider, Karl Mason · 15 de julio de 2026

  Federated Reinforcement Learning (FedRL) enables coordination of distributed energy resources without sharing raw local data, but standard aggregation methods such as FedAvg do not account for system-level constraints, often leading to unsafe global behavior. In this work, we study constraint-aware …

• Managing Task Execution for Unknown Workloads in Batteryless IoT: A Hardware-Agnostic Evaluation

  Samer Nasser, Henrique Duarte Moura, Ritesh Kumar Singh, Maarten Weyn, Jeroen Famaey · 24 de junio de 2026

  In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures. Sustaining reliable operation in these systems requires intelligent management of highly volatile stored energy. As edge applications grow in complexity, traditional energy-a…

• Toward Proactive RF Charging Scheduling: Generative AI for Decision Support

  Amirhossein Azarbahram, Osmel M. Rosabal, David Ernesto Ruiz-Guirola, Melike Erol-Kantarci, Kaibin Huang, Onel L. A. L\'opez · 10 de junio de 2026

  Radio frequency wireless power transfer (RF-WPT) is an enabling technology for supporting uninterrupted communications in future Internet of Things systems by reducing the need for battery replacement and mitigating battery-waste-related issues. For large-scale RF-WPT deployment, one of the main cha…

• Analog RF Computing: A New Paradigm for Energy-Efficient Edge AI Over MU-MIMO Systems

  Wentao Yu, Vincent W. S. Wong · 15 de mayo de 2026

  Modern edge devices increasingly rely on neural networks for intelligent applications. However, conventional digital computing-based edge inference requires substantial memory and energy consumption. In analog radio frequency (RF) computing, a base station (BS) encodes the weights of the neural netw…

• Planar Gaussian Splatting with Bilinear Spatial Transformer for Wireless Radiance Field Reconstruction

  Jinghan Zhang, Xitao Gong, Qi Wang, Richard A. Stirling-Gallacher, Giuseppe Caire · 30 de abril de 2026

  Wireless radiance field (WRF) reconstruction aims to learn a continuous, queryable representation of radio frequency characteristics over 3D space and direction, from which specific quantities, such as the spatial power spectrum (SPS) at a receiver given a transmitter position, can be predicted. Whi…

• Decentralized Federated Learning With Energy Harvesting Devices

  Kai Zhang, Xuanyu Cao, Khaled B. Letaief · 17 de febrero de 2026

  Decentralized federated learning (DFL) enables edge devices to collaboratively train models through local training and fully decentralized device-to-device (D2D) model exchanges. However, these energy-intensive operations often rapidly deplete limited device batteries, reducing their operational lif…

• A novel RF-enabled Non-Destructive Inspection Method through Machine Learning and Programmable Wireless Environments

  Stavros Tsimpoukis, Dimitrios Tyrovolas, Sotiris Ioannidis, Maria Kafesaki, Ian F. Akyildiz, George K. Karagiannidis, Christos K. Liaskos · 13 de enero de 2026

  Contemporary industrial Non-Destructive Inspection (NDI) methods require sensing capabilities that operate in occluded, hazardous, or access restricted environments. Yet, the current visual inspection based on optical cameras offers limited quality of service to that respect. In that sense, novel me…

• Pascal-Weighted Genetic Algorithms: A Binomially-Structured Recombination Framework

  Otman A. Basir · 2 de diciembre de 2025

  This paper introduces a new family of multi-parent recombination operators for Genetic Algorithms (GAs), based on normalized Pascal (binomial) coefficients. Unlike classical two-parent crossover operators, Pascal-Weighted Recombination (PWR) forms offsprings as structured convex combination of multi…