GS-SBL: Bridging Greedy Pursuit and Sparse Bayesian Learning for Efficient 3D Wireless Channel Modeling

TL;DR

This paper introduces GS-SBL, a novel sparse Bayesian learning framework that efficiently models 3D wireless channels by sequentially identifying virtual sources, combining the speed of greedy algorithms with the robustness of Bayesian methods.

Contribution

The paper proposes a Micro-SBL architecture that sequentially identifies sources, improving efficiency and accuracy over traditional SBL and OMP methods for 3D wireless channel modeling.

Findings

GS-SBL outperforms OMP in generalization on real-world data.

The method achieves real-time 3D path loss characterization.

Sequential source identification preserves Bayesian accuracy.

Abstract

Robust cognitive radio development requires accurate 3D path loss models. Traditional empirical models often lack environment-awareness, while deep learning approaches are frequently constrained by the scarcity of large-scale training datasets. This work leverages the inherent sparsity of wireless propagation to model scenario-specific channels by identifying a discrete set of virtual signal sources. We propose a novel Greedy Sequential Sparse Bayesian Learning (GS-SBL) framework that bridges the gap between the computational efficiency of Orthogonal Matching Pursuit (OMP) and the robust uncertainty quantification of SBL. Unlike standard top-down SBL, which updates all source hyperparameters simultaneously, our approach employs a ``Micro-SBL'' architecture. We sequentially evaluate candidate source locations in isolation by executing localized, low-iteration SBL loops and selecting the source that minimizes the $L_2$ residual error. Once identified, the source and its corresponding power are added to the support set, and the process repeats on the signal residual to identify subsequent sources. Experimental results on real-world 3D propagation data demonstrate that the GS-SBL framework significantly outperforms OMP in terms of generalization. By utilizing SBL as a sequential source identifier rather than a global optimizer, the proposed method preserves Bayesian high-resolution accuracy while achieving the execution speeds necessary for real-time 3D path loss characterization.