A Distributed Multi-Armed Bandit Approach for Modulation Adaptation in Underwater Networks

UnderWater (UW) communication channels pose unique challenges due to their limited bandwidth, significant temporal variability, and long transmission delays. Addressing these challenges calls for the implementation of robust protocols capable of dynamically adjusting transmission parameters in response to channel conditions. In such a perspective, the development of intelligent algorithms capable of quickly adapting to current channel conditions based on measurements is of crucial importance. Indeed, this allows to adapt the signal transmission characteristics to the channel conditions, and to accordingly ensure optimal performance. In this perspective, this paper introduces a Multi-Player Multi-Armed Bandit (MP-MAB) framework for smart modulation adaptation in UnderWater Acoustic (UWA) Networks. Our solution is specifically tailored to run on resource-constrained UW nodes, thanks to the simplicity and low-complexity of MAB. Our framework can leverage real-time throughput statistics to dynamically select the optimal modulation technique for multi-hop signal transmission in UW scenarios. Notably, this happens in a fully-distributed way on a per-node basis, as each UW node runs a local MAB agent to autonomously select the best modulation to use according to its own channel conditions. Using the DESERT UW simulator, we evaluate the performance of our proposed framework and compare it with alternative state-of-the-art learning approaches. Results demonstrate the higher efficiency and responsiveness of our algorithm compared to the alternatives, despite its simplicity and fully-decentralized nature.