具有有限反馈的自适应水下声学通信：一种面向信息年龄的分层多臂老虎机方法
Adaptive Underwater Acoustic Communications with Limited Feedback: An AoI-Aware Hierarchical Bandit Approach
AI总结：
本文提出一种分层多臂老虎机框架，通过结合信道状态反馈和信息年龄，优化自适应调制与发射功率，提高吞吐量并降低反馈开销，仿真结果表明相比深度强化学习基线，吞吐量提升20.61%，能耗降低36.60%。
摘要：
水下声学（UWA）网络对于远程传感和海洋探索至关重要，但面临带宽有限、传播延迟长和高度动态信道等固有挑战。为解决这些问题，本文提出一种双层多臂老虎机（MAB）框架。在快速内层，上下文延迟多臂老虎机（CD-MAB）基于信道状态反馈和信息年龄联合优化自适应调制和发射功率，从而最大化吞吐量。在较慢的外层，反馈调度MAB根据吞吐量动态调整信道状态反馈间隔：稳定吞吐量允许更长的更新间隔，而吞吐量下降触发更频繁的更新。这种自适应机制减少了反馈开销并增强了对变化网络条件的响应能力。所提出的双层框架计算高效，适用于资源受限的UWA网络。使用DESERT水下网络模拟器进行的仿真结果表明，与现有文献中报告的深度强化学习（DRL）基线相比，吞吐量提高了20.61%，能耗降低了36.60%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
