LWM-CDE: A Representation Space for Wireless Data Reasoning and Transferability
Machine learning deployments in real-world wireless communication tasks face significant generalization challenges due to location and environment-specific signal structure, high diversity in data across different deployments, and limited availability of real-world data. Current approaches for assessing data similarity between training and inference (deployment) distributions, as well as evaluating model transferability, suffer from high computational costs and inconsistent performance, leaving critical model deployment and model life cycle management decisions without a principled foundation. To address this, we introduce a dataset similarity framework built upon the feature space of a p...
| Authors | Sadjad Alikhani, Akshay Malhotra, Shahab Hamidi-Rad et al. |
|---|---|
| Primary category | eess.SP |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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