Performance analysis of UAV-assisted RF-EH and NOMA for D2D wireless sensor networks with kNN-based user selection

2026-02-25

Meyra Chusna Mayarakaca

Byung Moo Lee

DOI：10.23919/JCN.2025.000090

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Increasing demand for wireless sensor networks (WSNs) raises challenges in energy constraints and communication sustainability, especially in remote or disaster-affected areas where resources are limited. To address the constraints, this study proposes an integration of unmanned aerial vehicle (UAV)-assisted radio-frequency energy harvesting (RF-EH), non-orthogonal multiple access (NOMA), and device-to-device (D2D) communication to help with WSN energy constraints. The proposed system employs a UAV to transfer an RF signal for the WSN to perform EH, which will be used as a power source. The WSN operates in a D2D communication framework using NOMA communication. The system employs an EH time-switching (TS) protocol and dynamic power allocation to mitigate the impact of imperfect successive interference cancellation (SIC). The proposed system utilizes a machine learning-based k-nearest neighbor (kNN) algorithm to perform node selection at the UAV. Simulation results indicate that the kNN-based user selection improves the energy harvested by the WSN through the RF-EH process, which can reduce the BER by approximately 22.8%. Overall, the proposed system demonstrates superior performance by achieving lower bit error rate (BER) compared to conventional scenarios without the RF-EH process.

Keywords:

Energy harvesting (EH)

non-orthogonal multiple access (NOMA)

unmanned aerial vehicle (UAV)

wireless sensor networks (WSN)

Performance analysis of UAV-assisted RF-EH and NOMA for D2D wireless sensor networks with kNN-based user selection

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