  The emerging concept of channel twinning (CT) has great potential to become a
key enabler of ubiquitous connectivity in next-generation (xG) wireless
systems. By fusing multimodal sensor data, CT advocates a high-fidelity and
low-overhead channel acquisition paradigm, which is promising to provide
accurate channel prediction in cross-domain and high-mobility scenarios of
ubiquitous xG networks. However, the current literature lacks a universal CT
architecture to address the challenges of heterogeneous scenarios, data, and
resources in xG networks, which hinders the widespread deployment and
applications of CT. This article discusses a new modularized CT architecture to
bridge the barriers to scene recognition, cooperative sensing, and
decentralized training. Based on the modularized design of CT, universal
channel modeling, multimodal cooperative sensing, and lightweight twin modeling
are described. Moreover, this article provides a concise definition, technical
features, and case studies of CT, followed by potential applications of
CT-empowered ubiquitous connectivity and some issues requiring future
investigations.
