Voxel-CKM: Voxelized Radio Frequency Radiance Fields for Fast and Few-Shot CKM Construction

Channel knowledge maps (CKMs) are designed to predict channel state information (CSI) from user locations, thereby enabling low-overhead CSI acquisition. However, existing CKM construction methods often require hours-to-days of training time and dense measurements, resulting in substantial deployment cost. In this paper, we propose Voxel-CKM, a novel voxelized radio frequency (RF) radiance field framework for fast and few-shot CKM construction. The core idea is to replace implicit neural representations with explicit voxel grids to efficiently capture the spatial variation of wireless channels. Building upon this, we further introduce a compact vector-matrix (VM) decomposition to parameterize these voxel grids using a small set of matrices and vectors, which significantly accelerates convergence and facilitates fast CKM construction. To enable few-shot learning, we incorporate a transmitter prior as an inductive bias to guide the learning process under sparse measurements. Additionally, a total-variation (TV) regularization loss is proposed to mitigate overfitting and stabilize optimization. Experiments show that Voxel-CKM substantially accelerates training convergence and improves performance in the few-shot regime.