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Partially pruned DNN coupled with parallel Monte-Carlo algorithm for path loss prediction in underwater wireless
Optics Express
|April 27, 2022
Summary
This study introduces a novel method combining parallel Monte-Carlo simulations and a pruned deep neural network to accurately predict underwater optical path loss. This approach significantly speeds up data generation and compresses model size for efficient underwater wireless optical communication system design.
Area of Science:
- Underwater wireless optical communication
- Radiative transfer modeling
- Machine learning applications
Background:
- Underwater wireless optical channels face severe attenuation from absorption and scattering, impacting line-of-sight (LOS) propagation.
- Accurate path loss prediction is crucial for designing robust underwater wireless optical communication (UWOC) systems.
- Existing methods for solving the radiative transfer equation (RTE) can be computationally intensive.
Purpose of the Study:
- To propose and validate a novel approach for solving the RTE and determining path loss in UWOC systems.
- To accelerate dataset generation for path loss prediction using parallel Monte-Carlo (MC) simulations.
- To develop a compact and efficient deep neural network (DNN) for predicting received optical power in various water types.
Main Methods:
- A parallel MC algorithm was developed to significantly speed up dataset generation for path loss calculations.
- A partially pruned deep neural network (PPDNN) was employed to predict received optical power, optimizing network structure.
- The PPDNN was trained and tested on data simulating three typical water types: clear, coastal, and harbor.
Main Results:
- Parallel MC reduced dataset generation time by at least 95% compared to conventional MC.
- The PPDNN achieved mean square errors (MSEs) below 0.2 for path loss prediction across different water types.
- Model compression reached at least 40% storage reduction with minimal performance degradation, increasing DNN weight sparsity.
Conclusions:
- The proposed PPDNN approach offers an efficient and accurate method for predicting path loss in UWOC systems.
- The combination of parallel MC and PPDNN enables rapid and reliable estimation of received optical power.
- This technique facilitates the practical design and deployment of future underwater wireless optical communication systems.
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