Adaptive Switching for Multimodal Underwater Acoustic Communications Based on Reinforcement Learning
Adaptive Switching for Multimodal Underwater Acoustic Communications Based on Reinforcement Learning
复制标题
基于强化学习的多模态水声通信自适应切换
DOI：
10.1145/3491315.3491354
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Wang, Zhaohui
中科院分区：
文献类型：
--
作者：
Fan, Cheng;Wang, Zhaohui
水声信道是一个复杂的随机过程，具有很强的时空动态性。本文研究了沟通策略对渠道动态的适应性。具体而言，一组通信策略被认为是，包括频移键控（FSK），单载波通信，和多载波通信。基于信道条件，强化学习（RL）算法，深度确定的策略梯度（DDPG）方法沿着与Gumbel-softmax计划，用于智能和自适应切换这些通信策略。自适应切换是在逐块传输的基础上执行的，其目标是最大化长期系统性能。基于通信策略的能量效率和频谱效率来定义奖励函数。仿真结果表明，该方法优于随机选择方法在时变信道。
The underwater acoustic (UWA) channel is a complex and stochastic process with large spatial and temporal dynamics. This work studies the adaptation of the communication strategy to the channel dynamics. Specifically, a set of communication strategies are considered, including frequency shift keying (FSK), single-carrier communication, and multicarrier communication. Based on the channel condition, a reinforcement learning (RL) algorithm, the Depth Determined Strategy Gradient (DDPG) method along with a Gumbel-softmax scheme is employed for intelligent and adaptive switching among those communication strategies. The adaptive switching is performed on a transmission block-by-block basis, with the goal of maximizing a long-term system performance. The reward function is defined based on the energy efficiency and the spectral efficiency of the communication strategies. Simulation results reveal that the proposed method outperforms a random selection method in time-varying channels.
影响因子：
3.9
作者：
Chaofeng Wang;Zhaohui Wang;Wensheng Sun;D. Fuhrmann
通讯作者：
Chaofeng Wang;Zhaohui Wang;Wensheng Sun;D. Fuhrmann