Adaptive Underwater Acoustic Communications with Limited Feedback: An AoI-Aware Hierarchical Bandit Approach
2026-02-23 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors address the challenges of underwater acoustic networks, which struggle with limited bandwidth and delays, making communication difficult. They propose a two-level system where the inner level quickly adjusts how signals are sent based on the quality and freshness of the channel information, while the outer level changes how often this channel information is updated based on how stable the network is. This method reduces unnecessary feedback and adapts better to changing conditions. Their simulations show improved data rates and energy savings compared to existing learning methods.
Underwater Acoustic NetworksMulti-Armed BanditAdaptive ModulationTransmission PowerAge of InformationFeedback SchedulingThroughputEnergy EfficiencyDelayed FeedbackDESERT Underwater Network Simulator
Authors
Fabio Busacca, Andrea Panebianco, Yin Sun
Abstract
Underwater Acoustic (UWA) networks are vital for remote sensing and ocean exploration but face inherent challenges such as limited bandwidth, long propagation delays, and highly dynamic channels. These constraints hinder real-time communication and degrade overall system performance. To address these challenges, this paper proposes a bilevel Multi-Armed Bandit (MAB) framework. At the fast inner level, a Contextual Delayed MAB (CD-MAB) jointly optimizes adaptive modulation and transmission power based on both channel state feedback and its Age of Information (AoI), thereby maximizing throughput. At the slower outer level, a Feedback Scheduling MAB dynamically adjusts the channel-state feedback interval according to throughput dynamics: stable throughput allows longer update intervals, while throughput drops trigger more frequent updates. This adaptive mechanism reduces feedback overhead and enhances responsiveness to varying network conditions. The proposed bilevel framework is computationally efficient and well-suited to resource-constrained UWA networks. Simulation results using the DESERT Underwater Network Simulator demonstrate throughput gains of up to 20.61% and energy savings of up to 36.60% compared with Deep Reinforcement Learning (DRL) baselines reported in the existing literature.