# Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach

arXiv: https://arxiv.org/abs/2607.00056 · HTML: https://mlsanity.com/paper/2607.00056/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 85 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Builds on (most cited first, 27 of 27)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning](https://mlsanity.com/paper/1712.06567/) (2017)
- [KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics](https://mlsanity.com/paper/2107.10043/) (2021)
- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019)
- [Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari](https://mlsanity.com/paper/1802.08842/) (2018)
- [PyGAD: An Intuitive Genetic Algorithm Python Library](https://mlsanity.com/paper/2106.06158/) (2021)
- [Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2205.03793/) (2022)
- [Active Sensing for Communications by Learning](https://mlsanity.com/paper/2112.04075/) (2021)
- [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)
- [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)
- [Leveraging RIS-Enabled Smart Signal Propagation for Solving Infeasible Localization Problems](https://mlsanity.com/paper/2204.11538/) (2022)
- [Reconfigurable Intelligent Surfaces for 6G: Emerging Hardware Architectures, Applications, and Open Challenges](https://mlsanity.com/paper/2312.16874/) (2023)
- [Deep Contextual Bandits for Orchestrating Multi-User MISO Systems with Multiple RISs](https://mlsanity.com/paper/2202.08194/) (2022)
- [Active hypothesis testing in unknown environments using recurrent neural networks and model free reinforcement learning](https://mlsanity.com/paper/2303.10623/) (2023)
- [On the Impact of Control Signaling in RIS-Empowered Wireless Communications](https://mlsanity.com/paper/2303.16797/) (2023)
- [Localization with Reconfigurable Intelligent Surface: An Active Sensing Approach](https://mlsanity.com/paper/2312.09002/) (2023)
- [Asymptotically Optimal Closed-Form Phase Configuration of 1-bit RISs via Sign Alignment](https://mlsanity.com/paper/2407.13510/) (2024)
- [Towards Ubiquitous Positioning by Leveraging Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2007.05140/) (2020)
- [Joint Active RIS Configuration and User Power Control for Localization: A Neuroevolution-Based Approach](https://mlsanity.com/paper/2510.13819/) (2025)
- [MIMO Communications with 1-bit RIS: Asymptotic Analysis and Over-the-Air Channel Diagonalization](https://mlsanity.com/paper/2511.08534/) (2025)
- [Bayesian User Localization and Tracking for Reconfigurable Intelligent Surface Aided MIMO Systems](https://mlsanity.com/paper/2112.04729/) (2021)
- [Multi-Agent Actor-Critic with Harmonic Annealing Pruning for Dynamic Spectrum Access Systems](https://mlsanity.com/paper/2503.15172/) (2025)

## Abstract (arXiv)

This paper studies energy efficient tracking of power-limited mobile users with the assistance of a Reconfigurable Intelligent Surface (RIS). Since localization pilot transmissions dominate the energy budget of power-constrained devices, we introduce a low-overhead feedback link from the Base Station (BS) to the user to enable dynamic uplink power control. To navigate the discrete and decentralized nature of this active sensing problem, we propose a novel Dual-Agent (DA) deep learning framework that jointly optimizes the discrete RIS phase profiles and the UE's transmit power in real time. Specifically, our approach employs a hybrid training methodology integrating the neuroevolution paradigm with supervised learning, effectively overcoming the non-differentiability of discrete phase responses from the RIS unit elements and the strict information bottleneck of single-bit feedback messages for pilot power control. The proposed DA active sensing framework can be applied with both single- and multi-antenna BSs, the latter with only minor modifications in the structure of one NN: an additional output branch with appropriate structure is included for the latter case to select a valid digital combiner from a finite set. Extensive numerical simulations demonstrate that the proposed scheme achieves highly accurate and robust tracking across diverse target motion models, outperforming extended Kalman and particle filters, as well as, machine learning-based trackers. Furthermore, in static localization, it is shown to significantly outperform traditional fingerprinting schemes, deep reinforcement learning baselines, and standard backpropagation-based estimators.