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The demand on the underwater communications is extremely increasing insearching for underwater resources, marine expedition, or environmentalresearches, yet there are many problems with the wireless communicationsbecause of the characteristics of the underwater environments. Especially, withthe underwater wireless networks, there happen inevitable delay time andspacial inequality due to the distances between the nodes. To solve theseproblems, this paper suggests a new solution based on ALOHA-Q. The suggestedmethod use random NAV value. and Environments take reward throughcommunications success or fail. After then, The environments setting NAV valuefrom reward. This model minimizes usage of energy and computing resources underthe underwater wireless networks, and learns and setting NAV values throughintense learning. The results of the simulations show that NAV values can beenvironmentally adopted and select best value to the circumstances, so theproblems which are unnecessary delay times and spacial inequality can besolved. Result of simulations, NAV time decreasing 17.5% compared with originalNAV.

Q-NAV: NAV Setting Method based on Reinforcement Learning in Underwater Wireless Networks