Game-based hierarchical multi-armed bandit learning algorithm for joint channel and power allocation in underwater acoustic communication networks

Abstract

Abstract This study considers a joint channel and power allocation for multiple users in underwater acoustic communication networks as a formulated multiplayer MAB game. This study also proposes hierarchical learning algorithms, which do not need any prior environmental information and direct information exchange among users, to improve the learning ability. In upper sub-learning, each user generates a strategy through the traditional UCB1 strategy. In lower sub-learning, the concept of virtual learning information, which can be obtained as the reward of the last actual played strategy, is introduced to enrich the learning information. Users can enhance their learning ability by learning the outdated virtual learning information in lower sub-learning. As a result, the learning time it takes to achieve the NE is effectively decreased, and the cost of the algorithm is reduced. A distributed optimal NE selection mechanism is proposed to avoid falling into an inadequate local extreme value. Simulation results show high convergence speed and achieved utility of the proposed algorithm.