Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Angayarkanni, V.Subject: search.filters.subject.UWOC

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    Machine Learning Aided Signal Detection in Underwater Wireless Optical Communication for IoUT applications
    (Institute of Electrical and Electronics Engineers Inc., 2024) Kavitha, K.; Angayarkanni, V.; Paramanandham, N.; Yogarajan, G.; Krishnan, P.
    This study explores the effectiveness of the Machine Learning (ML) algorithms in addressing channel impairments in underwater Wireless Optical Communication (UWOC) systems employing On-Off Keying (OOK) modulation. The simulation takes into account underwater challenges such as signal attenuation, scattering, and absorption by using the Gamma-Gamma distribution to model fading and scintillation effects. The ML algorithm's performance is assessed by comparing its bit error rate (BER) against the signal-to-noise ratio (SNR) in comparison to the ideal scenario with perfect channel state information (CSI). The simulation covers various underwater scenarios, including ocean water, harbor water, clear water, coastal water, and oligotrophic water, showcasing the algorithm's adaptability in diverse environmental conditions. The results indicate that the SVM algorithm closely approaches the BER performance achieved with CSI, demonstrating its potential to improve communication reliability in UWOC systems under realistic channel conditions. This study provides valuable insights into the application of machine learning for signal detection in UWOC, offering prospects for enhanced underwater communication performance. © 2024 IEEE.
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    Performance Analysis of Triple Hop RF-RIS Convergent with FSO and UWOC System
    (Institute of Electrical and Electronics Engineers Inc., 2025) Sheeba, A.; Dharshini V, M.; Suvetha, P.; Kavitha, K.; Suveetha Dhanaselvam, P.; Karthikeyan, B.; Angayarkanni, V.; Krishnan, P.
    This paper proposes a hybrid communication system for integration within the space-air-ground-sea network (SAGSIN) architecture, tailored for 6G Internet of Things (6GIoT) applications. The system employs a triple-hop link combining Reconfigurable Intelligent Surface (RF-RIS), Free Space Optics (FSO), and Underwater Wireless Optical Communication (UWOC), facilitating communication between a base station, a lighthouse, a ship, and Underwater Aerial Vehicles (UWAVs). The architecture, using differential phaseshift keying, is ideal for Underwater Optical Wireless Sensor Networks (UOWSNs) and Internet of Underwater Things (IoUT). Performance analysis models RF-RIS with Nakagamim distribution, the FSO link with the Malaga distribution, and the UWOC link using the Exponential Generalized Gamma (EGG) distribution. The system's end-to-end cumulative distribution function (CDF) is evaluated via the Meijer-G function, while outage probability is examined under varying conditions, including atmospheric turbulence, fading parameters, and salinity levels. This system shows promise for coastal environments with changing weather conditions like rain, haze, and fog. © 2025 IEEE.