WiCo-MG: Wireless Channel Foundation Model for Multipath Generation via Synesthesia of Machines

TL;DR

WiCo-MG is a novel AI-based model that generates high-fidelity wireless multipath channel data, improving accuracy, generalization, and scalability for 6G communication system development.

Contribution

The paper introduces WiCo-MG, a new foundation model for multipath generation using Synesthesia of Machines, with a two-stage training framework and a novel dataset for UAV-to-ground communications.

Findings

Achieves state-of-the-art generation performance with NMSE reduction over baselines.

Demonstrates superior out-of-distribution generalization.

Shows strong scalability and extensibility to new parameters.

Abstract

Precise modeling of channel multipath is essential for understanding wireless propagation environments and optimizing communication systems. In particular, sixth-generation (6G) artificial intelligence (AI)-native communication systems demand massive and high-quality multipath channel data to enable intelligent model training and performance optimization. In this paper, we propose a wireless channel foundation model (WiCo) for multipath generation (WiCo-MG) via Synesthesia of Machines (SoM). To provide a solid training foundation for WiCo-MG, a new synthetic intelligent sensing-communication dataset for uncrewed aerial vehicle (UAV)-to-ground (U2G) communications is constructed. To overcome the challenges of cross-modal alignment and mapping, a two-stage training framework is proposed. In the first stage, sensing images are embedded into discrete-continuous SoM feature spaces, and multipath maps are embedded into a sensing-initialized discrete SoM space to align the representations. In the second stage, a mixture of shared and routed experts (S-R MoE) Transformer with frequency-aware expert routing learns the mapping from sensing to channel SoM feature spaces, enabling decoupled and adaptive multipath generation. Experimental results demonstrate that WiCo-MG achieves state-of-the-art in-distribution generation performance and superior out-of-distribution generalization, reducing NMSE by more than 2.59 dB over baselines, while exhibiting strong scalability in model and dataset growth and extensibility to new multipath parameters and tasks. Owing to higher accuracy, stronger generalization, and better scalability, WiCo-MG is expected to enable massive and high-fidelity channel data generation for the development of 6G AI-native communication systems.