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

TL;DR

This paper introduces the Path Evolution Model (PEM), a novel endogenous Channel Digital Twin for 6G wireless networks that requires only channel measurements, enabling high-precision, low-overhead CSI acquisition without external devices.

Contribution

The paper proposes PEM as an environment-agnostic, self-sustaining digital twin model that improves CSI acquisition in 6G MIMO systems without additional hardware or extensive training data.

Findings

PEM achieves high-precision CSI with low overhead.

PEM demonstrates strong environmental generalizability.

Simulation results confirm PEM's effectiveness for 6G networks.

Abstract

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 6G 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 (e.g., cameras and GPS 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, 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. Firstly, PEM only requires existing channel measurements, which is free of other hardware devices and can be readily deployed. Secondly, self-sustaining maintenance of PEM can be achieved in dynamic channel by progressive updates. Thirdly, environmental generalizability can greatly reduce deployment costs in dynamic environments. To facilitate the implementation of PEM, an intelligent and light-weighted operation framework is firstly designed. Then, the environmental generalizability of PEM is rigorously analyzed. Next, efficient learning approaches are proposed to reduce the amount of training data practically. 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.