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

TL;DR

This work leverages the inherent sparsity of wireless propagation to model scenario-specific channels by identifying a discrete set of virtual signal sources by identifying a discrete set of virtual signal sources through a novel Greedy Sequential Sparse Bayesian Learning framework.

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.

Figures (4)

• Figure 1: UAV-based signal strength measurements at 30 m and 110 m altitudes. The red triangle ($\triangle$) indicates the physical signal source, while the violet circles ($\circ$) denote the six predicted "virtual" source centers ($N_{\text{SBL}}=6$), each representing a 25 m $\times$ 25 m $\times$ 10 m spatial cell.
• Figure 2: Impact of the number of virtual sources $N_{\text{SBL}}$ on GS-SBL performance, measured in RMSE, across five measurement altitudes. Training altitudes (30 m and 110 m) are indicated by an asterisk (*) in the legend.
• Figure 3: Measured RSRP versus 3D distance compared with FSPL and the GS-SBL predictions at four altitudes. The training altitude for GS-SBL is marked with an asterisk (*).
• Figure 4: RMSE of the proposed GS-SBL, OMP, and FSPL under training-test elevation separations of $\{0, 10, 20\}$ m. An asterisk (*) on the X-axis indicates performance on the training set (0 m), while the rest represent the model's generalization performance on unseen test altitudes.