Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localization

• URL: http://arxiv.org/abs/2504.08811v1
• Date: Wed, 09 Apr 2025 03:36:52 GMT
• Title: Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localization
• Authors: Zirui Chen, Zhaoyang Zhang, Ziqing Xing, Ridong Li, Zhaohui Yang, Richeng Jin, Chongwen Huang, Yuzhi Yang, Mérouane Debbah,
• Abstract summary: This article proposes a brand-new universal deep learning framework named analogical learning (AL)<n>AL provides a highly efficient way to implicitly retrieve the reference frame information associated with a scenario.<n>We apply AL to the typical multi-scenario learning problem of intelligent wireless localization in cellular networks.
• Score: 45.64154370037619
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing learning models often exhibit poor generalization when deployed across diverse scenarios. It is mainly due to that the underlying reference frame of the data varies with the deployment environment and settings. However, despite the data of each scenario has its distinct reference frame, its generation generally follows the same underlying physical rule. Based on these findings, this article proposes a brand-new universal deep learning framework named analogical learning (AL), which provides a highly efficient way to implicitly retrieve the reference frame information associated with a scenario and then to make accurate prediction by relative analogy across scenarios. Specifically, an elegant bipartite neural network architecture called Mateformer is designed, the first part of which calculates the relativity within multiple feature spaces between the input data and a small amount of embedded data from the current scenario, while the second part uses these relativity to guide the nonlinear analogy. We apply AL to the typical multi-scenario learning problem of intelligent wireless localization in cellular networks. Extensive experiments show that AL achieves state-of-the-art accuracy, stable transferability and robust adaptation to new scenarios without any tuning, and outperforming conventional methods with a precision improvement of nearly two orders of magnitude. All data and code are available at https://github.com/ziruichen-research/ALLoc.

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