Adaptive Modulation in Underwater Acoustic Networks (AMUSE): A Multi-Armed Bandit Approach

Underwater (UW) communication channels experience limited bandwidth, high time variability and much longer delays as compared to traditional terrestrial channels. This makes communication in UW scenarios particularly challenging. One way to cope with this issue is to employ reliable smart protocols that can dynamically adjust transmission parameters based on channel conditions. The effectiveness of these solutions also relies on the design of intelligent algorithms that can forecast channel conditions based on measurements and adapt the transmission characteristics of signals according to the current state of the UW channel, in order to always guarantee the best performance. In this work we propose AMUSE, the first Multi-Armed Bandit-based algorithm for smart modulation adaptation in Underwater Acoustic Networks. Note that AMUSE is specifically designed to suit resource-constrained UW nodes thanks to its simplicity and low-complexity. In particular, AMUSE relies on the current Packet Delivery Ratio (PDR) statistics to select in real time the best modulation technique to use for multihop signal transmission in the aforementioned UW scenarios. By employing the DESERT simulator we compare the performance achieved using AMUSE to those obtained using alternative state-of-the-art learning approaches. The results show that, in spite of its simplicity, our algorithm is more efficient and responsive than the other considered approaches.