Conference Papers

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

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Author: search.filters.author.Krishnan, P.Subject: search.filters.subject.UWOC

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    Analysis and Enhancement of Spectral Efficiency of UWOC System for IoUT Applications
    (Institute of Electrical and Electronics Engineers Inc., 2023) Mitra, A.; Kumar, A.; Krishnan, P.
    Underwater wireless optical communication (UWOC) has engrossed significant attention in diverse fields, such as defence, military applications, environmental monitoring, and scientific research. The growing number of interconnected devices in underwater environments has increased the importance of UWOC capacity analysis. It will be instrumental in underwater optical wireless sensor networks (UOWSNs) and the Internet of Underwater Things (IoUT). Understanding the concept of underwater channel capacity is crucial as it determines the maximum volume of reliable information that can be sent through the underwater communication channel. This work focuses on analyzing the performance of a UWOC system that facilitates communication between a surface source (ship) and an autonomous underwater vehicle (AUV) as the intended recipient. The investigation considers explicitly heterodyne detection and models the channel with Exponential Generalized Gamma (EGG) distribution. The study presents closed-form expressions for average channel capacity, employing Meijer's G function and derived average spectral efficiency (ASE) based on the derived channel capacity. Additionally, the paper delves into the influence of various factors, including pointing errors, bubble levels, water types, beam waist, and aperture radius, on the system's overall performance. © 2023 IEEE.
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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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    Outage Probability Analysis of Dual Hop FSO Convergent with RF-UWOC Hybrid Link for SAGSIN in 6G-IoT Systems
    (Institute of Electrical and Electronics Engineers Inc., 2024) Rajeshwari, V.; Ishana Chellam, S.; Yukta Sri, C.K.; Kavitha, K.; Suveetha Dhanaselvam, P.; Karthikeyan, B.; Krishnan, P.
    This paper analyses the effectiveness of a novel hybrid communication system designed for a space-air-ground-sea integrated network (SAGSIN) within 6G Internet of Things (6G-IoT) environments. The system employs a dual-hop mechanism, featuring a free-space optical (FSO) link with a Malaga distribution for communication between ground and ships, along with a hybrid Radio Frequency - Underwater Wireless Optical Communication (RF-UWOC) link characterized by Nakagami-m and exponential generalized Gamma distributions for ship-to-underwater Autonomous Vehicle (AUV) communication. In this configuration, the ship serves as a Decode-and-Forward relay, where the RF link bolsters system reliability, and the UWOC link ensures secure communication. To optimize overall performance, the system implements a selection combining technique to identify the best signal from both RF and UWOC links. The paper evaluates the end-to-end cumulative distribution function (CDF) of the channel through the Meijer-G hypergeometric function. It also examines the outage probability of the proposed system across various weather conditions in the FSO channel, the effects of the Nakagami fading parameter m, and variations in salinity in the UWOC link. © 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.