# Autoregressive Attention Neural Networks for Non-Line-of-Sight User Tracking with Dynamic Metasurface Antennas

arXiv: https://arxiv.org/abs/2310.19767 · HTML: https://mlsanity.com/paper/2310.19767/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 34 references that are in the map.
- First posted on arXiv 30 Oct 2023.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Dynamic Metasurface Antennas for 6G Extreme Massive MIMO Communications](https://mlsanity.com/paper/2006.07838/) (2020)
- [Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2205.03793/) (2022)
- [DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm](https://mlsanity.com/paper/2109.10581/) (2021)
- [Phase Configuration Learning in Wireless Networks with Multiple Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2010.04376/) (2020)
- [Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques](https://mlsanity.com/paper/2203.03176/) (2022)
- [RISs and Sidelink Communications in Smart Cities: The Key to Seamless Localization and Sensing](https://mlsanity.com/paper/2301.03535/) (2023)
- [Hybrid Reconfigurable Intelligent Metasurfaces: Enabling Simultaneous Tunable Reflections and Sensing for 6G Wireless Communications](https://mlsanity.com/paper/2104.04690/) (2021)
- [WiThRay: A Versatile Ray-Tracing Simulator for Smart Wireless Environments](https://mlsanity.com/paper/2304.11385/) (2023)
- [Active Sensing for Localization with Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2310.13160/) (2023)
- [Reconfigurable, Intelligent, and SustainableWireless Environments for 6G Smart Connectivity](https://mlsanity.com/paper/2110.07547/) (2021)
- [Large Intelligent Surface for Positioning in Millimeter Wave MIMO Systems](https://mlsanity.com/paper/1910.00060/) (2019)
- [Hybrid Beamforming for RIS-Empowered Multi-hop Terahertz Communications: A DRL-based Method](https://mlsanity.com/paper/2009.09380/) (2020)
- [MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning](https://mlsanity.com/paper/2011.12515/) (2020)
- [Localization via Multiple Reconfigurable Intelligent Surfaces Equipped with Single Receive RF Chains](https://mlsanity.com/paper/2202.13939/) (2022)
- [3D Localization with a Single Partially-Connected Receiving RIS: Positioning Error Analysis and Algorithmic Design](https://mlsanity.com/paper/2212.02088/) (2022)
- [Compressed-Sensing-Based 3D Localization with Distributed Passive Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2210.15242/) (2022)
- [Full Duplex Holographic MIMO for Near-Field Integrated Sensing and Communications](https://mlsanity.com/paper/2303.07792/) (2023)
- [Learning to Localize with Attention: from sparse mmWave channel estimates from a single BS to high accuracy 3D location](https://mlsanity.com/paper/2307.00167/) (2023)
- [Sparse Recovery with Attention: A Hybrid Data/Model Driven Solution for High Accuracy Position and Channel Tracking at mmWave](https://mlsanity.com/paper/2308.13908/) (2023)

## Abstract (arXiv)

User localization and tracking in the upcoming generation of wireless networks have the potential to be revolutionized by technologies such as the Dynamic Metasurface Antennas (DMAs). Commonly proposed algorithmic approaches rely on assumptions about relatively dominant Line-of-Sight (LoS) paths, or require pilot transmission sequences whose length is comparable to the number of DMA elements, thus, leading to limited effectiveness and considerable measurement overheads in blocked LoS and dynamic multipath environments. In this paper, we present a two-stage machine-learning-based approach for user tracking, specifically designed for non-LoS multipath settings. A newly proposed attention-based Neural Network (NN) is first trained to map noisy channel responses to potential user positions, regardless of user mobility patterns. This architecture constitutes a modification of the prominent vision transformer, specifically modified for extracting information from high-dimensional frequency response signals. As a second stage, the NN's predictions for the past user positions are passed through a learnable autoregressive model to exploit the time-correlated channel information and obtain the final position predictions. The channel estimation procedure leverages a DMA receive architecture with partially-connected radio frequency chains, which results to reduced numbers of pilots. The numerical evaluation over an outdoor ray-tracing scenario illustrates that despite LoS blockage, this methodology is capable of achieving high position accuracy across various multipath settings.