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April 3, 2026SensorsOpen Access

Joint Optimization of Time Slot and Power Allocation in Underwater Acoustic Communication Networks

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Authors

XGXuan GengYHYongkang Hu

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Overview

This analysis demonstrates a joint optimization algorithm for time slot and power allocation in underwater acoustic networks, suggesting enhanced channel capacity.

Key Points

  • This research addresses optimizing time slot and power allocation in underwater acoustic communication networks to improve transmission capacity.
  • Developed a joint optimization algorithm using reinforcement learning.
  • Constructed a Markov Decision Process (MDP) model based on Deep Q-Network (DQN) for time slot allocation.
  • Implemented the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for power allocation among transmission nodes.
  • Conducted simulations to compare the performance of the proposed algorithm with TDMA, Slotted ALOHA, and others.
  • The proposed algorithm significantly increases the number of successfully transmitted links.
  • Channel capacity is maximized under energy limitation compared to traditional algorithms.
  • Simulation results indicate superior performance in both time slot and power allocations.

Cite This Study

Geng et al. (2026) studied this question.

synapsesocial.com/papers/69cf5f505a333a821460e6d1https://doi.org/10.3390/s26072188
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