Path Evolution Model for Endogenous Channel Digital Twin Toward 6G Wireless Networks

Massive multiple-input multiple-output (MIMO) is critical for boosting 6G wireless network capacity. Nevertheless, high-dimensional channel state information (CSI) acquisition becomes the bottleneck of a massive MIMO system. Recently, channel digital twin (CDT), which replicates physical entities in wireless channels, has been proposed, providing site-specific prior knowledge for CSI acquisition. However, external devices cannot always be integrated into existing communication systems, nor are they universally available across all scenarios. Moreover, the trained CDT model cannot be directly applied in new environments, which lacks environmental generalizability. To this end, the path evolution model (PEM) is proposed as an alternative CDT to reflect physical path evolutions from consecutive channel measurements. Compared to existing CDTs, PEM demonstrates virtues of full endogeneity, self-sustainability, and environmental generalizability. First, PEM only requires existing channel measurements, which are free of other hardware devices and can be readily deployed. Secondly, the self-sustaining maintenance of PEM can be achieved through progressive updates in dynamic channels. Thirdly, environmental generalizability can greatly reduce costs for large-scale deployment. To facilitate the implementation of PEM, an intelligent and lightweight operation framework is first designed. Then, the environmental generalizability of PEM is rigorously analyzed, which can effectively address the distribution shift across environments. Extensive simulation results reveal that PEM can simultaneously achieve high-precision and low-overhead CSI acquisition, which can serve as a fundamental CDT for 6G wireless networks.