Communication Dans Un Congrès Année : 2024

Resource Allocation for IRS-Assisted Symbiotic Vehicular RSMA and Backscatter Communication

Résumé

Under precision agriculture (PA), plant disease detection (PDD) is imperative regarding farm crops' life quality and crop yield. However, the data captures for PDD is influenced by the noisy data captured by farm sensors due to wireless noisy transmission channels. Hence, this work considers the onsite or offsite (remote) farm PDD through onsite farm monitoring PA sensor networks (PAN). Here, effects on captured sensor image (plant leaf image) data transmitted through the PAN to an PDD application are studied. Where both traditional decodeand-forward (DF) data routing and channel-effect considering machine learning data autoencoder routing are used for image data transmission. In addition, a PDD deep learning algorithm is developed to predict whether or not a farm plant is diseased, based on the noisy image data captured by the PAN through data routing. From the PAN-PDD simulation, the proposed ML PAN-PDD algorithm showed fair performance over the DF PAN-PDD.
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Dates et versions

hal-04910102 , version 1 (24-01-2025)

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Kofi Ofori-Amanfo, Derek Kwaku Pobi Asiedu, Samir Saoudi, Ji-Hoon Yun. Resource Allocation for IRS-Assisted Symbiotic Vehicular RSMA and Backscatter Communication. 2024 15th International Conference on Information and Communication Technology Convergence (ICTC), Oct 2024, Jeju Island, South Korea. pp.1169-1170, ⟨10.1109/ICTC62082.2024.10827565⟩. ⟨hal-04910102⟩
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