AI 中文总结
提出任务感知无线电世界模型 RMWorld，结合信息价值校准与多试选择，在多无人机通信控制任务中，于 100 次 3GPP 试次和 30 次 DeepMIMO 试次上取得了更优的速率误差与积压控制效果。
AI 中文摘要
可靠的多无人机通信控制依赖于在获取测量值之前预测哪些空中链路将用于流量传输。无线电世界模型（radio WMs）使此类规划变得可行，但其误差分布不均：全局准确的模型仍可能在高需求走廊或关联边界处失效，这些地方的速率误差会反转控制决策。这种不匹配构成了学习挑战：链路查询必须减少与决策相关的信道不确定性，而反事实试次必须经过筛选，以避免有偏差的 rollout 破坏策略。现有的获取和基于模型的控制将这些预算分开处理，重视不确定性、覆盖率或乐观回报，而非风险降低。我们提出 RMWorld，这是一个任务感知的 radio-WM 框架，它将信息价值（value-of-information，VoI）信道校准与可信度多样性多试选择相结合。有偏差的传播公式通过贝叶斯残差进行修正，每条链路的价值由其在局部线性化的任务集成后验速率方差中精确的单标签减少量来衡量。反事实分支通过任务门控对数行列式目标选择，随后进行冲突投影和固定批次验证。我们推导了方差减少恒等式，证明了后验任务风险等价性和次模贪心保证，并建立了范围一阶无干扰结果。在 100 次配对 3GPP 试次中，RMWorld 达到了 0.949 bit/s/Hz 的任务加权均方根误差；在 30 次严重负载的 DeepMIMO 试次中，与 Ensemble UCB 相比，它在离线 rollout 增加 37.5% 的情况下，将中位数积压减少了 0.967。
英文摘要
Reliable multi-UAV communication control depends on predicting which aerial links will serve traffic before measurements are available. Radio world models (radio WMs) make such planning tractable, but their errors are nonuniform: a globally accurate model may still fail along high-demand corridors or association boundaries where rate errors reverse control decisions. This mismatch creates a learning challenge. Link queries must reduce decision-relevant channel uncertainty, while counterfactual trials must be filtered so that biased rollouts do not corrupt the policy. Existing acquisition and model-based control treat these budgets separately, valuing uncertainty, coverage, or optimistic return rather than risk reduction. We present RMWorld, a task-aware radio-WM framework that couples value-of-information channel calibration with credibility-diversity multi-trial selection. A biased propagation formula is corrected by a Bayesian residual, and each link is valued by its exact one-label reduction in locally linearized task-integrated posterior rate variance. Counterfactual branches are selected by a task-gated log-determinant objective, followed by conflict projection and fixed-batch validation. We derive the variance-reduction identity, prove posterior task-risk equivalence and the submodular greedy guarantee, and establish a scoped first-order non-interference result. Across 100 paired 3GPP trials RMWorld reaches 0.949~bit/s/Hz task-weighted RMSE, and across 30 severe-load DeepMIMO trials it reduces median backlog by 0.967 versus Ensemble UCB at 37.5\% more offline rollouts.
