Flow Matching for Fast Channel Knowledge Map Construction in AI-Native 6G Networks

Abstract

Channel knowledge maps (CKMs) or radio maps are a key enabler for AI-native 6G networks, supporting proactive resource allocation, coverage optimization, and environment-aware wireless intelligence. Recent generative approaches have demonstrated improved realism and structural fidelity compared with purely discriminative regression, but often require many iterative sampling steps and incur non-negligible latency in dynamic scenarios. Motivated by the need for fast and high-fidelity CKM construction, we propose a conditional Flow Matching (FM) framework for channel gain map generation from lightweight environment priors. Specifically, our model takes as conditioning inputs (i) a binary/gray-scale building occupancy map and (ii) a transmitter (Tx) location mask, and learns a continuous-time probability flow that transports noise to the target channel gain map. We implement the flow field with a time-conditioned U-Net enhanced by multi-scale context aggregation and attention mechanisms (ASPP + CBAM), improving the model’s capability to capture sharp shadowing boundaries and long-range propagation patterns. At inference, the channel gain map is generated by solving an ODE with a small number of function evaluations, yielding efficient generating while preserving spatial structure. The proposed FM-based CKM constructor provides a promising alternative to diffusion models for workshop settings targeting wireless foundation models and AI-native 6G. The code is avilable at https://github.com/AiBiaoZ/FM-CKM