# Learning Beamforming Codebooks for Active Sensing with Reconfigurable Intelligent Surface

arXiv: https://arxiv.org/abs/2503.19046 · HTML: https://mlsanity.com/paper/2503.19046/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 48 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Builds on (most cited first, 28 of 28)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming](https://mlsanity.com/paper/1810.03961/) (2018)
- [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019)
- [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)
- [RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials](https://mlsanity.com/paper/2103.00534/) (2021)
- [Active Sensing for Communications by Learning](https://mlsanity.com/paper/2112.04075/) (2021)
- [Intelligent Surfaces Empowered Wireless Network: Recent Advances and The Road to 6G](https://mlsanity.com/paper/2312.16918/) (2023)
- [Active Sensing for Localization with Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2310.13160/) (2023)
- [Adaptive Beamforming Design for mmWave RIS-Aided Joint Localization and Communication](https://mlsanity.com/paper/1911.02813/) (2019)
- [Codebook Design and Beam Training for Extremely Large-Scale RIS: Far-Field or Near-Field?](https://mlsanity.com/paper/2109.10143/) (2021)
- [Joint Training of the Superimposed Direct and Reflected Links in Reconfigurable Intelligent Surface Assisted Multiuser Communications](https://mlsanity.com/paper/2105.14484/) (2021)
- [Learning-Based Adaptive IRS Control with Limited Feedback Codebooks](https://mlsanity.com/paper/2112.01874/) (2021)
- [VQ-T: RNN Transducers using Vector-Quantized Prediction Network States](https://mlsanity.com/paper/2208.01818/) (2022)
- [Localization with Reconfigurable Intelligent Surface: An Active Sensing Approach](https://mlsanity.com/paper/2312.09002/) (2023)
- [Learning Reflection Beamforming Codebooks for Arbitrary RIS and Non-Stationary Channels](https://mlsanity.com/paper/2109.14909/) (2021)
- [Convolutional Autoencoder-Based Phase Shift Feedback Compression for Intelligent Reflecting Surface-Assisted Wireless Systems](https://mlsanity.com/paper/2110.12348/) (2021)
- [Learning Based User Scheduling in Reconfigurable Intelligent Surface Assisted Multiuser Downlink](https://mlsanity.com/paper/2205.06396/) (2022)
- [Codebook-Based Solutions for Reconfigurable Intelligent Surfaces and Their Open Challenges](https://mlsanity.com/paper/2211.05976/) (2022)
- [Interference Nulling Using Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2112.13261/) (2021)
- [Phase Shift Compression for Control Signaling Reduction in IRS-Aided Wireless Systems: Global Attention and Lightweight Design](https://mlsanity.com/paper/2207.01908/) (2022)
- [A Novel Two-Layer Codebook Based Near-Field Beam Training for Intelligent Reflecting Surface](https://mlsanity.com/paper/2303.06962/) (2023)
- [Active Sensing for Two-Sided Beam Alignment and Reflection Design Using Ping-Pong Pilots](https://mlsanity.com/paper/2305.07130/) (2023)
- [Hierarchical Codebook Design and Analytical Beamforming Solution for IRS Assisted Communication](https://mlsanity.com/paper/2308.01515/) (2023)
- [Active Sensing for Multiuser Beam Tracking with Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2405.03129/) (2024)
- [Environment-Aware Codebook Design for RIS-Assisted MU-MISO Communications: Implementation and Performance Analysis](https://mlsanity.com/paper/2406.09058/) (2024)
- [Localization in Multipath Environments via Active Sensing with Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2406.19483/) (2024)

## Abstract (arXiv)

This paper explores the design of beamforming codebooks for the base station (BS) and for the reconfigurable intelligent surfaces (RISs) in an active sensing scheme for uplink localization, in which the mobile user transmits a sequence of pilots to the BS through reflection at the RISs, and the BS and the RISs are adaptively configured by carefully choosing BS beamforming codeword and RIS codewords from their respective codebooks in a sequential manner to progressively focus onto the user. Most existing codebook designs for RIS are not tailored for active sensing, by which we mean the choice of the next codeword should depend on the measurements made so far, and the sequence of codewords should dynamically focus reflection toward the user. Moreover, most existing codeword selection methods rely on exhaustive search in beam training to identify the codeword with the highest signal-to-noise ratio (SNR), thus incurring substantial pilot overhead as the size of the codebook scales. This paper proposes a learning-based approach for codebook construction and for codeword selection for active sensing. The proposed learning approach aims to locate a target in the service area by recursively selecting a sequence of BS beamforming codewords and RIS codewords from the respective codebooks as more measurements become available without exhaustive beam training. The codebook design and the codeword selection fuse key ideas from the vector quantized variational autoencoder (VQ-VAE) and the long short-term memory (LSTM) network to learn respectively the discrete function space of the codebook and the temporal dependencies between measurements.