无线数据集相似性：在监督和无监督机器学习中测量距离
Wireless Dataset Similarity: Measuring Distances in Supervised and Unsupervised Machine Learning
AI总结：
本文提出了一种无线数据集相似性测量框架，通过任务和模型感知的方法，提升跨数据集迁移性和模型适应性评估的准确性。
摘要：
本文介绍了一个任务和模型感知的框架，用于测量无线数据集之间的相似性，使数据集选择/增强、模拟到现实（sim2real）比较、任务特定的合成数据生成以及指导模型训练和适应新部署的决策成为可能。我们通过候选数据集距离度量在预测跨数据集迁移性方面的表现来评估这些度量：如果两个数据集的距离较小，一个在其中一个上训练的模型应在另一个上表现良好。我们将其应用于一个无监督任务，即信道状态信息（CSI）压缩，使用自编码器。使用基于UMAP嵌入的度量，结合Wasserstein和欧几里得距离，我们实现了数据集距离与训练-测试任务性能之间的皮尔逊相关系数超过0.85。我们还将其应用于下行链路中的监督波束预测，使用卷积神经网络。对于此任务，我们推导出一种标签感知的距离，通过整合监督UMAP和对数据集不平衡的惩罚。在两个任务中，所得到的距离优于传统基线，并且始终表现出更强的与模型迁移性相关的相关性，支持无线数据集之间的任务相关比较。
英文摘要：
This paper introduces a task- and model-aware framework for measuring similarity between wireless datasets, enabling applications such as dataset selection/augmentation, simulation-to-real (sim2real) comparison, task-specific synthetic data generation, and informing decisions on model training/adaptation to new deployments. We evaluate candidate dataset distance metrics by how well they predict cross-dataset transferability: if two datasets have a small distance, a model trained on one should perform well on the other. We apply the framework on an unsupervised task, channel state information (CSI) compression, using autoencoders. Using metrics based on UMAP embeddings, combined with Wasserstein and Euclidean distances, we achieve Pearson correlations exceeding 0.85 between dataset distances and train-on-one/test-on-another task performance. We also apply the framework to a supervised beam prediction in the downlink using convolutional neural networks. For this task, we derive a label-aware distance by integrating supervised UMAP and penalties for dataset imbalance. Across both tasks, the resulting distances outperform traditional baselines and consistently exhibit stronger correlations with model transferability, supporting task-relevant comparisons between wireless datasets.
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。
