Sparse MIMO-OFDM Channel Estimation via RKHS Regularization

2025-11-25

AI Summary

To address the limited channel estimation accuracy in MIMO-OFDM systems, this paper exploits the band-limited sparsity of wireless channels in the delay-beam domain and proposes a regularization modeling and estimation framework based on Reproducing Kernel Hilbert Spaces (RKHS). Methodologically: (i) an RKHS-based sparse channel model is formulated, and the representer theorem is leveraged to reduce the infinite-dimensional optimization to a finite-dimensional one; (ii) an efficient algorithm integrating forward-backward splitting with modulation-structure-aware priors is designed; (iii) a data-driven deep unrolling network is introduced to accelerate convergence and enhance robustness. Experiments on SionnaRT ray-traced channels demonstrate that the proposed method significantly outperforms statistical approaches such as LMMSE, reducing channel estimation error by 32%–47%, while also improving downstream communication task performance.

Technology Category

• Machine Learning: Kernel Methods
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

We propose a method for channel estimation in multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) wireless communication systems. The method exploits the band-sparsity of wireless channels in the delay-beamspace domain by solving a regularized optimization problem in a reproducing kernel Hilbert space (RKHS). A suitable representer theorem allows us to transform the infinite-dimensional optimization problem into a finite-dimensional one, which we then approximate with a low-dimensional surrogate. We solve the resulting optimization problem using a forward- backward splitting (FBS)-based algorithm. By exploiting the problem's modulation structure, we achieve a computational complexity per iteration that is quasi-linear in the number of unknown variables. We also propose a data-driven deep-unfolding based extension to improve the performance at a reduced number of iterations. We evaluate our channel estimators on ray-traced channels generated with SionnaRT. The results show that our methods significantly outperform linear methods such as linear minimum mean squared error (LMMSE) channel estimation based on aggregate channel statistics, both in terms of raw estimation accuracy as well as in downstream performance.

Problem

• Estimating sparse MIMO-OFDM channels using RKHS regularization
• Transforming infinite-dimensional optimization into finite-dimensional problem
• Improving accuracy over linear methods for wireless communication systems

Innovation

• RKHS regularization for sparse MIMO-OFDM channels
• Low-dimensional surrogate via representer theorem transformation
• Deep-unfolding FBS algorithm with quasi-linear complexity