# Hybrid-Field Sparse Channel Representation and Recovery for XL-RIS-Assisted mmWave MIMO Systems

arXiv: https://arxiv.org/abs/2608.00052 · HTML: https://mlsanity.com/paper/2608.00052/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 26 Jul 2026.

## Builds on (most cited first, 19 of 19)

- [A Survey on Channel Estimation and Practical Passive Beamforming Design for Intelligent Reflecting Surface Aided Wireless Communications](https://mlsanity.com/paper/2110.01292/) (2021)
- [RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials](https://mlsanity.com/paper/2103.00534/) (2021)
- [Structure-aware Sparse Bayesian Learning-based Channel Estimation for Intelligent Reflecting Surface-aided MIMO](https://mlsanity.com/paper/2210.11373/) (2022)
- [Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals](https://mlsanity.com/paper/1311.2150/) (2013)
- [Radio Localization and Mapping with Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1912.09401/) (2019)
- [Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement](https://mlsanity.com/paper/1911.05326/) (2019)
- [Channel Estimation for RIS-Aided mmWave MIMO Systems via Atomic Norm Minimization](https://mlsanity.com/paper/2007.08158/) (2020)
- [Two-Timescale Channel Estimation for Reconfigurable Intelligent Surface Aided Wireless Communications](https://mlsanity.com/paper/1912.07990/) (2019)
- [Terahertz Massive MIMO with Holographic Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2009.10963/) (2020)
- [Physics-based Modeling and Scalable Optimization of Large Intelligent Reflecting Surfaces](https://mlsanity.com/paper/2004.12957/) (2020)
- [Reconfigurable-Intelligent-Surface Empowered Wireless Communications: Challenges and Opportunities](https://mlsanity.com/paper/2001.00364/) (2020)
- [Codebook Design and Beam Training for Extremely Large-Scale RIS: Far-Field or Near-Field?](https://mlsanity.com/paper/2109.10143/) (2021)
- [Localization via Multiple Reconfigurable Intelligent Surfaces Equipped with Single Receive RF Chains](https://mlsanity.com/paper/2202.13939/) (2022)
- [Low-overhead Beam Training Scheme for Extremely Large-Scale RIS in Near-field](https://mlsanity.com/paper/2211.15910/) (2022)
- [Near-Field Sparse Channel Representation and Estimation in 6G Wireless Communications](https://mlsanity.com/paper/2212.13527/) (2022)
- [Off-Grid Aware Channel and Covariance Estimation in mmWave Networks](https://mlsanity.com/paper/1907.04423/) (2019)
- [Variational Learning Algorithms For Channel Estimation in RIS-assisted mmWave Systems](https://mlsanity.com/paper/2211.14495/) (2022)
- [Channel Estimation for RIS-Aided MIMO Systems: A Partially Decoupled Atomic Norm Minimization Approach](https://mlsanity.com/paper/2307.11403/) (2023)
- [Efficient Off-Grid Near-Field Cascade Channel Estimation for XL-IRS Systems via Tucker Decomposition](https://mlsanity.com/paper/2602.13988/) (2026)

## Abstract (arXiv)

Extremely large-scale reconfigurable intelligent surface (XL-RIS)-assisted communication is regarded as a key enabling technology for future 6G networks. However, hybrid-field channel estimation for XL-RIS-assisted systems is challenging due to the high-dimensional cascaded channel and the coexistence of far-field and near-field propagation. In this case, traditional full-dimensional sparse recovery methods require a large cascaded dictionary and suffer from severe computational and storage burdens. To address these challenges, we develop a double-timescale channel estimation framework that decouples sparse dictionary representation and recovery. Then, by exploiting the quasi-static property of the channel at the base station (BS) and RIS side, we propose a Dirichlet kernel-based off-grid dictionary compression (DK-ODC) scheme for sparse representation, which reduces the dimension of the corresponding dictionary as well as mitigates BS-side angular off-grid error. Furthermore, for the dynamic channel at the user equipment (UE) and RIS side, we propose a subspace-aware incremental variational Bayesian learning (SI-VBL) algorithm, which enables incremental learning of sparse channels by exploiting the identified low-dimensional subspace and pruning threshold. Analysis and simulation results confirm that the proposed framework avoids full-dimensional Bayesian recovery and achieves a favorable tradeoff among estimation accuracy, computational complexity, and storage overhead.