Offset Pointing for Energy-efficient Reception in Underwater Optical Wireless Communication: Modeling and Performance Analysis

TL;DR

A comprehensive stochastic geometry framework to perform a differential energy analysis of UOWC links and shows that the optimal offset strategy can reduce the required transmit power by nearly 20\% to achieve a target BER, which translates into extended network lifetime and higher total data throughput.

Abstract

Underwater Wireless Optical Communication is a key enabling technology for future space-air-ground-sea integrated networks. However, UOWC faces critical hurdles from spatial randomness and stringent energy constraints. These challenges fundamentally limit network lifetime and sustainability. This paper develops a comprehensive stochastic geometry framework to perform a differential energy analysis of UOWC links.Instead of relying on simplified models, we employ a three-dimensional truncated Poisson point process to accurately capture the anisotropic nature of the underwater environment, specifically the disparity between horizontal spread and vertical depth. It incorporates a Lambertian emission pattern, random receiver positions and orientations, and a realistic channel model with extinction effects. Under this model, we derive a full suite of closed-form expressions for key performance indicators. These include the nearest-neighbor distance distribution, expected received power, SNR, and BER. A principal and counter-intuitive finding of our analysis is an offset-pointing strategy. This strategy involves intentionally misaligning the receiver by a deterministically optimal angle. This approach maximizes the integrated received power across the aperture, contrary to the conventional pursuit of perfect alignment. We formulate and solve an energy-efficiency optimization problem. Our results demonstrate that this strategy enhances system robustness and yields substantial performance gains. Simulation results validate our analytical models. They show that the optimal offset strategy can reduce the required transmit power by nearly 20\% to achieve a target BER. This reduction directly translates into extended network lifetime and higher total data throughput. These findings offer a new design paradigm for deploying robust, cost-effective, and sustainable UOWC networks.

Figures (12)

• Figure 1: Model of actual underwater optical communication
• Figure 2: Mathematical model of a pair of Tx and Rx in underwater optical communication
• Figure 3: Comparison between closed-form solution and numerical solution for \ref{['eq:15']}
• Figure 4: Geometric models, mathematical definitions, and parameter representations related to Tx and Rx
• Figure 5: Comparison between closed-form solution and numerical solution for optimal $\delta$ in \ref{['eq:42']}

Theorems & Definitions (23)

• Proposition 1: Survival Function and PDF of Nearest Neighbor Distance
• proof
• Theorem 1: Expected Vertical Position of Nearest Neighbor
• proof
• Remark 1
• Lemma 1: Approximation for Large Distance
• proof
• Theorem 2: Average Received Power with Random Orientation
• Remark 2
• proof