# Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Data

arXiv: https://arxiv.org/abs/2405.06299 · HTML: https://mlsanity.com/paper/2405.06299/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 75 references that are in the map.
- First posted on arXiv 10 May 2024.

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Gradient Descent Finds Global Minima of Deep Neural Networks](https://mlsanity.com/paper/1811.03804/) (2018)
- [Are Transformers universal approximators of sequence-to-sequence functions?](https://mlsanity.com/paper/1912.10077/) (2019)
- [Bridging Theory and Algorithm for Domain Adaptation](https://mlsanity.com/paper/1904.05801/) (2019)
- [Multi-Time Attention Networks for Irregularly Sampled Time Series](https://mlsanity.com/paper/2101.10318/) (2021)
- [Graph-Guided Network for Irregularly Sampled Multivariate Time Series](https://mlsanity.com/paper/2110.05357/) (2021)
- [Which Neural Net Architectures Give Rise To Exploding and Vanishing Gradients?](https://mlsanity.com/paper/1801.03744/) (2018)
- [Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation](https://mlsanity.com/paper/2010.04647/) (2020)
- [Reconfigurable Intelligent Surfaces: Principles and Opportunities](https://mlsanity.com/paper/2007.03435/) (2020)
- [A Survey on Fundamental Limits of Integrated Sensing and Communication](https://mlsanity.com/paper/2104.09954/) (2021)
- [Enabling Joint Communication and Radar Sensing in Mobile Networks -- A Survey](https://mlsanity.com/paper/2006.07559/) (2020)
- [6G Wireless Channel Measurements and Models: Trends and Challenges](https://mlsanity.com/paper/2012.06381/) (2020)
- [Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities](https://mlsanity.com/paper/2005.00938/) (2020)
- [MUSE-Fi: Contactless MUti-person SEnsing Exploiting Near-field Wi-Fi Channel Variation](https://mlsanity.com/paper/2308.10234/) (2023)
- [Terahertz Massive MIMO with Holographic Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2009.10963/) (2020)
- [Leveraging RIS-Enabled Smart Signal Propagation for Solving Infeasible Localization Problems](https://mlsanity.com/paper/2204.11538/) (2022)
- [Adaptive Beamforming Design for mmWave RIS-Aided Joint Localization and Communication](https://mlsanity.com/paper/1911.02813/) (2019)
- [Channel Estimation with Reconfigurable Intelligent Surfaces -- A General Framework](https://mlsanity.com/paper/2110.00553/) (2021)
- [HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning](https://mlsanity.com/paper/2310.06336/) (2023)
- [MetaLocalization: Reconfigurable Intelligent Surface Aided Multi-user Wireless Indoor Localization](https://mlsanity.com/paper/2011.09323/) (2020)
- [Path Loss Modeling and Measurements for Reconfigurable Intelligent Surfaces in the Millimeter-Wave Frequency Band](https://mlsanity.com/paper/2101.08607/) (2021)
- [AI-Assisted MAC for Reconfigurable Intelligent Surface-Aided Wireless Networks: Challenges and Opportunities](https://mlsanity.com/paper/2105.00437/) (2021)
- [Collaborative Sensing in Perceptive Mobile Networks: Opportunities and Challenges](https://mlsanity.com/paper/2205.15805/) (2022)
- [Joint Active and Passive Beamforming Design for Reconfigurable Intelligent Surface Enabled Integrated Sensing and Communication](https://mlsanity.com/paper/2206.00525/) (2022)

## Built on by (most cited first, 4 of 4)

- [Cross-Domain Continual Learning for Edge Intelligence in Wireless ISAC Networks](https://mlsanity.com/paper/2502.12736/) (2025)
- [Multi-Functional RIS-Enabled in SAGIN for IoT: A Hybrid Deep Reinforcement Learning Approach with Compressed Twin-Models](https://mlsanity.com/paper/2507.16204/) (2025)
- [Meta-SimGNN: Adaptive and Robust WiFi Localization Across Dynamic Configurations and Diverse Scenarios](https://mlsanity.com/paper/2511.14076/) (2025)
- [When Agentic AI Meets Integrated Sensing and Communication](https://mlsanity.com/paper/2608.05792/) (2026)

## Abstract (arXiv)

Integrated sensing and communications (ISAC) is pivotal for 6G communications and is boosted by the rapid development of reconfigurable intelligent surfaces (RISs). Using the channel state information (CSI) across multiple frequency bands, RIS-aided multi-band ISAC systems can potentially track users' positions with high precision. Though tracking with CSI is desirable as no communication overheads are incurred, it faces challenges due to the multi-modalities of CSI samples, irregular and asynchronous data traffic, and sparse labeled data for learning the tracking function. This paper proposes the X2Track framework, where we model the tracking function by a hierarchical architecture, jointly utilizing multi-modal CSI indicators across multiple bands, and optimize it in a cross-domain manner, tackling the sparsity of labeled data for the target deployment environment (namely, target domain) by adapting the knowledge learned from another environment (namely, source domain). Under X2Track, we design an efficient deep learning algorithm to minimize tracking errors, based on transformer neural networks and adversarial learning techniques. Simulation results verify that X2Track achieves decimeter-level axial tracking errors even under scarce UL data traffic and strong interference conditions and can adapt to diverse deployment environments with fewer than 5% training data, or equivalently, 5 minutes of UE tracks, being labeled.