Generative Digital Twin Channel: Bridging Fully-Decoupled RAN and AI-Native Solutions

Fully-decoupled RAN (FD-RAN) has emerged as one of the most promising architectures for next-generation mobile communication networks to enable flexible resource utilization and cater to diverse user requirements, owing to its complete decoupling of control, uplink and downlink entities. AI-native solutions, including AI-assisted air interfaces and mobility management, hold significant promise for achieving these objectives. However, the efficacy of such solutions hinges on accurate wireless channel knowledge. To address this challenge, we introduce the concept of a Digital Twin Channel (DTC), which leverages ray tracing to generate channels based on digitalized environments. To mitigate the complexity of constructing precise DTCs in practical scenarios, we propose the Generative Digital Twin Channel (GDTC). This approach employs generative AI (GAI) models for environment reconstruction and radio wave propagation modeling, effectively empowering FD-RAN. We further present a comprehensive GDTC pipeline designed to produce accurate channels from environment and support decision-making across various communication tasks. To illustrate the capabilities of GDTC, we demonstrate through a case study to realize the feedback-free transmission task, which represents a promising paradigm envisioned for 6G. We utilize the vector quantized-variational autoencoder (VQ-VAE) to recover a specific channel accurately, as well as generate the representative channel optimized for feedback-free transmission.