Adaptive OFDM underwater acoustic transmission: An adversarial bandit approach

Abstract

Adaptive orthogonal frequency-division multiplexing (OFDM) is a promising technology for underwater acoustic sensor networks (UASNs) to facilitate robust and reliable transmission. This paper deals with an adaptive UASN-OFDM multi-parameter allocation problem in a strongly incomplete information scenario. Specifically, an adversarial multi-armed bandit (MAB) formalism is first proposed, whereby no prior knowledge about channel conditions is required and the reward sequences are not restrained by any statistical assumptions. Second, considering the curse of dimensionality caused by exponentially large number of feasible strategies, we tailor orthogonal learning strategy to reinforce learning for initial decision set and achieve filtration by abandoning some inferior levels. Third, under strictly limited prior information, we design a time-based dynamic exploration mechanism to adjust exploration factor adaptively, which improves algorithm learning ability effectively. Thank to aforementioned efforts, a low-complexity, high-efficiency OD-Exp3 algorithm is presented to handle the complex adaptive OFDM problem in UASNs. Lastly, we show the upper regret bound and the convergence of OD-Exp3 algorithm. Comparative results demonstrate that the proposed algorithm is superior to the existing algorithms.