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SLIPT for Underwater IoT: System Modeling and Performance Analysis

Shunyuan Shang, Ziyuan Shi, Mohamed-Slim Alouini

TL;DR

A composite statistical channel is developed that combines distance dependent absorption, turbulence induced fading, and beam misalignment due to pointing errors characterized by the mixture Exponential Generalized Gamma (EGG) distribution, and beam misalignment due to pointing errors.

Abstract

This paper presents a unified analytical framework for a two phase underwater wireless optical communication (UWOC) system that integrates Simultaneous Lightwave Information and Power Transfer (SLIPT) using a photovoltaic (PV) panel receiver. The proposed architecture enables self powered underwater sensor nodes by leveraging wide area and low cost PV panels for concurrent optical signal detection and energy harvesting. We develop a composite statistical channel that combines distance dependent absorption, turbulence induced fading characterized by the mixture Exponential Generalized Gamma (EGG )distribution, and beam misalignment due to pointing errors. Based on this model we derive closed form expressions for the probability density function, the cumulative distribution function, the outage probability (OP), the average bit error rate, the ergodic capacity, and the harvested power using Meijer G and Fox H functions. Overall, the paper introduces a practical analytical framework that provides clear guidance for design, optimization, and operation of SLIPT based UWOC systems.

SLIPT for Underwater IoT: System Modeling and Performance Analysis

TL;DR

A composite statistical channel is developed that combines distance dependent absorption, turbulence induced fading, and beam misalignment due to pointing errors characterized by the mixture Exponential Generalized Gamma (EGG) distribution, and beam misalignment due to pointing errors.

Abstract

This paper presents a unified analytical framework for a two phase underwater wireless optical communication (UWOC) system that integrates Simultaneous Lightwave Information and Power Transfer (SLIPT) using a photovoltaic (PV) panel receiver. The proposed architecture enables self powered underwater sensor nodes by leveraging wide area and low cost PV panels for concurrent optical signal detection and energy harvesting. We develop a composite statistical channel that combines distance dependent absorption, turbulence induced fading characterized by the mixture Exponential Generalized Gamma (EGG )distribution, and beam misalignment due to pointing errors. Based on this model we derive closed form expressions for the probability density function, the cumulative distribution function, the outage probability (OP), the average bit error rate, the ergodic capacity, and the harvested power using Meijer G and Fox H functions. Overall, the paper introduces a practical analytical framework that provides clear guidance for design, optimization, and operation of SLIPT based UWOC systems.

Paper Structure

This paper contains 25 sections, 8 theorems, 48 equations, 9 figures, 5 tables.

Table of Contents

  1. Introduction
  2. System Model
  3. Modeling Assumptions, Justification, and Applicability
  4. Signal and Power Models
  5. Channel Model
  6. Attenuation Loss
  7. Air‐Bubble and Temperature‐Induced Fading
  8. Pointing Error Loss
  9. Composite Channel Model
  10. Performance Analysis
  11. Harvested Power Analysis
  12. PDF of the Composite Channel Gain
  13. PDF and CDF of SNR
  14. Outage Probability
  15. SNR Moments
  16. ...and 10 more sections

Key Result

Theorem 1

The PDF of the channel gain from the submarine to the submerged sensor $h_1 = h_a h_t h_p$ is given by where $G_{p,q}^{ m,n}[\cdot]$ is the Meijer‐G function. Remarks: The Meijer G function is widely used in analytical modeling of FSO channels because it unifies many special functions and probability laws under a single framework shang2024enhancingzedini2014performanceshang2025novelshangOIRS. It

Figures (9)

  • Figure 1: Diagram of the two phase underwater optical wireless communication system. (a) Charging phase. (b) Communication phase.
  • Figure 2: Comparison between analytical and simulation results of the PDFs of single-link channel gain $h_1$ and composite channel gain $h = h_1 h_2$.
  • Figure 3: Average harvested power at the underwater sensor $\overline{P}_s$ versus average transmitted power from the submarine $P_t$ for different link distances $d_{vs}$.
  • Figure 4: OP of the return link transmission versus the average return link SNR $\overline{\gamma}_u$ for different submarine–sensor distances $d_{vs}$.
  • Figure 5: OP of the return link transmission versus the average return link SNR $\overline{\gamma}_u$ under different underwater channel types (Type 1–6).
  • ...and 4 more figures

Theorems & Definitions (17)

  • Theorem 1
  • proof
  • Corollary 1.1
  • proof
  • Corollary 1.2
  • proof
  • Theorem 2
  • proof
  • Theorem 3
  • proof
  • ...and 7 more