Low-Complexity Channel Estimation for Internet of Vehicles AFDM Communications With Sparse Bayesian Learning

2025-12-16

AI Summary

To address the low channel estimation accuracy and high computational complexity of AFDM-based vehicular networks under doubly dispersive channels, this paper proposes a dynamic grid-update framework based on sparse Bayesian learning (SBL), pioneering the integration of off-grid modeling with adaptive grid evolution. Two core algorithms are introduced: Grid-Refinement SBL (GR-SBL), achieving high-accuracy estimation, and Grid-Evolution SBL (GE-SBL), balancing estimation performance and computational efficiency. Furthermore, distributed parallel architectures—D-GR-SBL and D-GE-SBL—are developed to significantly reduce computational overhead via task decomposition. Experimental results demonstrate that GR-SBL attains near-optimal estimation accuracy; GE-SBL achieves a superior trade-off between accuracy and complexity; and the distributed variants maintain performance close to their centralized counterparts while drastically lowering complexity. This work establishes a tunable, scalable, and high-precision channel estimation paradigm tailored for real-time vehicular communications.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Machine Learning: Distributed Machine Learning & Federated Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• 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

Abstract

Affine frequency division multiplexing (AFDM) has been considered as a promising waveform to enable high-reliable connectivity in the internet of vehicles. However, accurate channel estimation is critical and challenging to achieve the expected performance of the AFDM systems in doubly-dispersive channels. In this paper, we propose a sparse Bayesian learning (SBL) framework for AFDM systems and develop a dynamic grid update strategy with two off-grid channel estimation methods, i.e., grid-refinement SBL (GR-SBL) and grid-evolution SBL (GE-SBL) estimators. Specifically, the GR-SBL employs a localized grid refinement method and dynamically updates grid for a high-precision estimation. The GE-SBL estimator approximates the off-grid components via first-order linear approximation and enables gradual grid evolution for estimation accuracy enhancement. Furthermore, we develop a distributed computing scheme to decompose the large-dimensional channel estimation model into multiple manageable small-dimensional sub-models for complexity reduction of GR-SBL and GE-SBL, denoted as distributed GR-SBL (D-GR-SBL) and distributed GE-SBL (D-GE-SBL) estimators, which also support parallel processing to reduce the computational latency. Finally, simulation results demonstrate that the proposed channel estimators outperform existing competitive schemes. The GR-SBL estimator achieves high-precision estimation with fine step sizes at the cost of high complexity, while the GE-SBL estimator provides a better trade-off between performance and complexity. The proposed D-GR-SBL and D-GE-SBL estimators effectively reduce complexity and maintain comparable performance to GR-SBL and GE-SBL estimators, respectively.

Problem

• Develop low-complexity channel estimation for AFDM in vehicular networks
• Propose sparse Bayesian learning methods to handle doubly-dispersive channels
• Reduce computational complexity via distributed and parallel processing schemes

Innovation

• Sparse Bayesian learning framework for AFDM channel estimation
• Dynamic grid update strategy with GR-SBL and GE-SBL estimators
• Distributed computing scheme for complexity reduction and parallel processing