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

arXiv: https://arxiv.org/abs/2504.08811 · HTML: https://mlsanity.com/paper/2504.08811/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 54 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [On Layer Normalization in the Transformer Architecture](https://mlsanity.com/paper/2002.04745/) (2020)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Deep learning to represent sub-grid processes in climate models](https://mlsanity.com/paper/1806.04731/) (2018)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023)
- [Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather](https://mlsanity.com/paper/2210.15176/) (2022)
- [Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions](https://mlsanity.com/paper/2308.06250/) (2023)
- [CSI-based Positioning in Massive MIMO systems using Convolutional Neural Networks](https://mlsanity.com/paper/1911.11523/) (2019)
- [MetaLoc: Learning to Learn Wireless Localization](https://mlsanity.com/paper/2211.04258/) (2022)
- [Transferring Foundation Models for Generalizable Robotic Manipulation](https://mlsanity.com/paper/2306.05716/) (2023)
- [Deep Convolutional Neural Networks for Massive MIMO Fingerprint-Based Positioning](https://mlsanity.com/paper/1708.06235/) (2017)
- [Novel Massive MIMO Channel Sounding Data Applied to Deep Learning-based Indoor Positioning](https://mlsanity.com/paper/1810.04126/) (2018)
- [A Distributed Massive MIMO Channel Sounder for "Big CSI Data"-driven Machine Learning](https://mlsanity.com/paper/2206.15302/) (2022)
- [In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models](https://mlsanity.com/paper/2311.06101/) (2023)
- [Learning to Localize: A 3D CNN Approach to User Positioning in Massive MIMO-OFDM Systems](https://mlsanity.com/paper/1910.12378/) (2019)
- [Transformers are Provably Optimal In-context Estimators for Wireless Communications](https://mlsanity.com/paper/2311.00226/) (2023)
- [Positioning Using Wireless Networks: Applications, Recent Progress and Future Challenges](https://mlsanity.com/paper/2403.11417/) (2024)
- [Deep Transfer Learning for WiFi Localization](https://mlsanity.com/paper/2103.05123/) (2021)
- [Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model](https://mlsanity.com/paper/2412.09041/) (2024)
- [Channel Deduction: A New Learning Framework to Acquire Channel from Outdated Samples and Coarse Estimate](https://mlsanity.com/paper/2403.19409/) (2024)
- [Multi-task Learning-based CSI Feedback Design in Multiple Scenarios](https://mlsanity.com/paper/2204.12698/) (2022)

## Built on by (most cited first, 3 of 3)

- [Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications](https://mlsanity.com/paper/2506.18419/) (2025)
- [Multi-View Wireless Sensing via Conditional Generative Learning: Framework and Model Design](https://mlsanity.com/paper/2505.12664/) (2025)
- [Geometry-Aided Channel Deduction with Partial Channel Estimates and Uncalibrated Digital Twin](https://mlsanity.com/paper/2609.37277/) (2026)

## Abstract (arXiv)

Modern learning systems often struggle with joint learning across diverse scenarios and immediate adaptation to new ones, because they rely heavily on the scenario-dependent absolute data-label representations. Here, we propose analogical learning (AL), a learning framework that explores the inherent invariance of the underlying physical processes across scenarios, to improve the cross-scenario generalization. Specifically, we introduce the physical concepts of reference frames and relativity into the neural modeling. The resultant framework explicitly employs intra-scenario data-label pairs as reference anchors and enforces the network to mediate its data-to-label transformation through data-domain relative metrics that factor out the scenario-dependent variations. We instantiate AL with Mateformer, a bipartite Transformer-based neural architecture. Each layer of the auxiliary Transformer extracts certain feature space of the current data, while the corresponding layer of the primary Transformer computes attention among the data feature space and then use it as a relativity metric to weight the current label feature to synthesize the next label feature and, ultimately, the final prediction. We apply AL to intelligent wireless localization, a representative multi-scenario learning task. Across synthetic, real-world, and city-scale datasets, AL enables robust cross-scenario transfer and multi-scenario joint learning, achieving wavelength-scale localization accuracy that matches or surpasses state-of-the-art methods. This physics-inspired learning framework provides a promising alternative for other cross-scenario learning tasks and applications.