论文标题
Q-NAV：基于水下无线网络中强化学习的NAV设置方法
Q-NAV: NAV Setting Method based on Reinforcement Learning in Underwater Wireless Networks
论文作者
论文摘要
在寻找水下资源，海洋探险或环境研究方面，对水下通信的需求极大地增加了，但是由于水下环境的特征，无线通信存在许多问题。尤其是，由于水下无线网络，由于节点之间的距离，会发生不可避免的延迟时间和空间不平等。为了解决这些问题，本文提出了一种基于Aloha-Q的新解决方案。建议的方法使用随机NAV值。环境通过沟通成功或失败而获得奖励。之后，环境从奖励中设置了NAV值。该模型最大程度地降低了水下无线网络下能源和计算资源的使用情况，并通过强烈的学习来学习和设定NAV值。模拟的结果表明，NAV值可以在环境上采用并为情况选择最佳价值，因此可以解决不必要的延迟时间和空间不平等的问题。模拟的结果，与原始NAV相比，NAV时间减少了17.5％。
The demand on the underwater communications is extremely increasing in searching for underwater resources, marine expedition, or environmental researches, yet there are many problems with the wireless communications because of the characteristics of the underwater environments. Especially, with the underwater wireless networks, there happen inevitable delay time and spacial inequality due to the distances between the nodes. To solve these problems, this paper suggests a new solution based on ALOHA-Q. The suggested method use random NAV value. and Environments take reward through communications success or fail. After then, The environments setting NAV value from reward. This model minimizes usage of energy and computing resources under the underwater wireless networks, and learns and setting NAV values through intense learning. The results of the simulations show that NAV values can be environmentally adopted and select best value to the circumstances, so the problems which are unnecessary delay times and spacial inequality can be solved. Result of simulations, NAV time decreasing 17.5% compared with original NAV.
