发表机构
Arizona State University（亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对现有波束预测框架依赖持续新鲜传感数据的缺陷，提出结合信息年龄（AoI）与知识蒸馏（KD）的传感预算约束下的辅助波束预测框架，实验验证了其在低数据量下的优异预测性能。
AI 中文摘要
利用环境数据的波束预测可降低空中波束训练开销，但现有框架假设能持续获取新鲜传感数据，该假设在传感预算受限或传感器故障时不成立。为提升实用性，本文提出平均传感速率约束下的辅助波束预测框架，将信息年龄（AoI）作为合成输入模态直接融入波束预测流程：对最新采集数据的年龄进行编码，通过门控机制与视觉特征融合，为预测器提供输入传感数据可靠性的显式上下文信息。本文在平均传感预算下，形式化分析并研究了累积、均匀、随机三种固定采样策略；进一步开发知识蒸馏（KD）框架，其作为鲁棒性正则化器而非单纯的模型压缩方法：在全采样数据上无约束训练大容量教师模型，将其表征知识迁移至部署在传感预算下的紧凑学生模型。在 DeepSense 6G 数据集上开展数值实验，结果显示，在严格传感预算下，AoI 融合使 top-1 准确率近乎翻倍；年龄感知模型仅用 20% 的数据即可达到接近最优的 top-3 准确率；此外，教师训练机制是比蒸馏损失函数更关键的设计选择。
英文摘要
Beam prediction leveraging environmental data reduces over-the-air beam training overhead. Existing frameworks, however, assume continuous access to fresh sensory data, an assumption that breaks down under sensing budget constraints or sensor failures. To make this more practical, this paper proposes a sensing-aided beam prediction framework that operates under an average sensing rate constraint. We incorporate the age of information (AoI) directly into the beam prediction pipeline as a synthetic input modality: the age of the most recently captured data is encoded and fused with visual features through a gating mechanism. This provides the predictor with explicit context information about input sensory data reliability. We formalize and examine three fixed sampling policies, accumulated, uniform, and randomized, under the average sensing budget. We further develop a knowledge distillation (KD) framework that operates as a robustness regularizer rather than a pure model compression method. In particular, the high-capacity teacher is trained unconstrained on fully sampled data, and its representational knowledge is transferred to a compact student deployed under the sensing budget. We conduct numerical experiments on the DeepSense 6G data set. The results show that AoI fusion nearly doubles top-1 accuracy at strict sensing budgets, and age-aware models achieve near-optimal top-3 accuracy with only 20% of the data. Furthermore, we find that the teacher training regime is a more consequential design choice than the distillation loss function.
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