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Alissa Nauman

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Open access Oct 2026

Learning to Configure Passive RIS on High-Altitude Platform Stations for Energy-Efficient IoT Communications

Internet of Things (IoT) devices operating in remote, rural, or disaster-affected areas often suffer from blocked or weak connections to terrestrial Base Stations (BSs). This paper proposes a High Altitude Platform Stations (HAPSs)-assisted uplink architecture in which a passive Reconfigurable Intelligent Surface (RIS)...

Alissa Nauman, Tahir Khurshaid, S. Rhee · 0 citations
Open access Aug 2026

Deep Reinforcement Learning-Based Energy-Efficient Resource Allocation and Scheduling in 6G-Enabled UAV-Assisted IoT Wireless Networks

A Deep Reinforcement Learning (DRL) framework that jointly optimizes user scheduling, IoT device transmit power, bandwidth, and UAV movement in a 6G-enabled UAV-relay uplink network, using a deterministic large-scale air-to-ground path-loss channel model.

Alissa Nauman, Sung Won Kim · 0 citations

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