PRB-WM：用于实时时变水声信道预测链路自适应的物理正则化Beta世界模型
PRB-WM: Physics-Regularized Beta World Models for Predictive Link Adaptation on Real Time-Varying Underwater Acoustic Channels
AI总结：
该研究针对水声链路自适应反馈滞后问题，提出首个水声通信世界模型PRB-WM，结合物理正则化等技术，在海试数据实验中性能远超基线方法。
摘要：
自适应调制与编码（AMC）可提升水声（UWA）链路的频谱效率，但其反馈不可避免存在滞后：声波传播缓慢，当信道状态信息（CSI）到达时，快速时变的多径信道已发生变化。因此，预测链路自适应不仅需要点预测，还需得到每个候选动作后果的分布。我们提出PRB-WM，一种用于实时时变UWA信道的预测性子载波链路自适应的物理正则化Beta世界模型。该模型包含：以持续性为锚点的随机隐空间动力学模型，用于学习信道演化规律；反事实结果头，用于针对每个候选调制与编码方案（MCS），评估共享想象未来中误码的Beta-二项分布；以及机会约束后退时域模型预测控制器，用于在误码率（BER）违规概率有界的约束下最大化期望频谱效率。据我们所知，PRB-WM是首个用于UWA通信的世界模型。在对来自公开UWA信道库的6条实测海试链路进行闭环重放，且对比覆盖全方法谱的24种方法池的实验中，PRB-WM的平均有效吞吐量较最强基线高出87%，在最长预测时域下的6条时变链路中均排名第一，且是唯一实现的帧风险随预测时域增加而系统性降低的方法，满足BER违规约束，而无任何基线方法能做到这一点。 ablation实验证实，展开采样离散度对机会约束至关重要，且物理锚点对可训练性是必要的。
英文摘要：
Adaptive modulation and coding (AMC) improves the spectral efficiency of underwater acoustic (UWA) links, but its feedback is inevitably stale: sound propagates slowly, and by the time channel state information (CSI) arrives, the fast time-varying multipath channel has moved on. Predictive link adaptation therefore needs more than a point forecast: it needs a distribution over each candidate action's consequences. We propose PRB-WM, a physics-regularized Beta world model for predictive per-subcarrier link adaptation on real time-varying UWA channels. A persistence-anchored stochastic latent-space dynamics model learns how the channel evolves; a counterfactual outcome head evaluates, for every candidate modulation and coding scheme (MCS), a Beta-binomial distribution of bit errors on shared imagined futures; and a chance-constrained receding-horizon model predictive controller maximizes expected spectral efficiency subject to a bounded probability of violating the bit error rate (BER) target. To the best of our knowledge, PRB-WM is the first world model for UWA communications. In closed-loop replay of six measured sea-trial links from the public UWA Channel Library against a 24-method pool spanning the full methodological spectrum, PRB-WM attains a mean goodput 87% above the strongest baseline, ranks first on all six time-varying links at the longest lookahead, and is the only method whose realized frame risk decreases systematically with lookahead, meeting the BER-violation contract where no baseline does. Ablations confirm that rollout sampling dispersion is essential to the chance constraint and that the physics anchor is necessary for trainability.
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