# Localization with Reconfigurable Intelligent Surface: An Active Sensing Approach

arXiv: https://arxiv.org/abs/2312.09002 · HTML: https://mlsanity.com/paper/2312.09002/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Wireless Communications Through Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1906.09490/) (2019)
- [Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead](https://mlsanity.com/paper/2004.09352/) (2020)
- [Learning to Reflect and to Beamform for Intelligent Reflecting Surface with Implicit Channel Estimation](https://mlsanity.com/paper/2009.14404/) (2020)
- [Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks](https://mlsanity.com/paper/1912.11999/) (2019)
- [6G White Paper on Localization and Sensing](https://mlsanity.com/paper/2006.01779/) (2020)
- [Active Learning and CSI Acquisition for mmWave Initial Alignment](https://mlsanity.com/paper/1812.07722/) (2018)
- [Active Sensing for Communications by Learning](https://mlsanity.com/paper/2112.04075/) (2021)
- [An Overview of Signal Processing Techniques for RIS/IRS-aided Wireless Systems](https://mlsanity.com/paper/2112.05989/) (2021)
- [Reconfigurable Intelligent Surfaces for Localization: Position and Orientation Error Bounds](https://mlsanity.com/paper/2009.02818/) (2020)
- [Active Sensing for Localization with Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2310.13160/) (2023)
- [Near-field Localization with a Reconfigurable Intelligent Surface Acting as Lens](https://mlsanity.com/paper/2010.05617/) (2020)
- [Adaptive Beamforming Design for mmWave RIS-Aided Joint Localization and Communication](https://mlsanity.com/paper/1911.02813/) (2019)
- [SISO RIS-Enabled Joint 3D Downlink Localization and Synchronization](https://mlsanity.com/paper/2011.02391/) (2020)
- [MetaLocalization: Reconfigurable Intelligent Surface Aided Multi-user Wireless Indoor Localization](https://mlsanity.com/paper/2011.09323/) (2020)
- [Towards Ubiquitous Positioning by Leveraging Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2007.05140/) (2020)
- [Trilateration-Based Device-Free Sensing: Two Base Stations and One Passive IRS Are Sufficient](https://mlsanity.com/paper/2205.12667/) (2022)
- [Beyond 5G Wireless Localization with Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2008.11431/) (2020)

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

- [Joint Active RIS Configuration and User Power Control for Localization: A Neuroevolution-Based Approach](https://mlsanity.com/paper/2510.13819/) (2025)
- [Learning Beamforming Codebooks for Active Sensing with Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2503.19046/) (2025)
- [Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach](https://mlsanity.com/paper/2607.00056/) (2026)

## Abstract (arXiv)

This paper addresses an uplink localization problem in which a base station (BS) aims to locate a remote user with the help of reconfigurable intelligent surfaces (RISs). We propose a strategy in which the user transmits pilots sequentially and the BS adaptively adjusts the sensing vectors, including the BS beamforming vector and multiple RIS reflection coefficients based on the observations already made, to eventually produce an estimated user position. This is a challenging active sensing problem for which finding an optimal solution involves searching through a complicated functional space whose dimension increases with the number of measurements. We show that the long short-term memory (LSTM) network can be used to exploit the latent temporal correlation between measurements to automatically construct scalable state vectors. Subsequently, the state vector is mapped to the sensing vectors for the next time frame via a deep neural network (DNN). A final DNN is used to map the state vector to the estimated user position. Numerical result illustrates the advantage of the active sensing design as compared to non-active sensing methods. The proposed solution produces interpretable results and is generalizable in the number of sensing stages. Remarkably, we show that a network with one BS and multiple RISs can outperform a comparable setting with multiple BSs.