Advancing Ubiquitous Wireless Connectivity through Channel Twinning

Abstract

As an emerging trend in channel acquisition (CA), the concept of channel twinning (CT) has been proposed as a powerful enabler of ubiquitous connectivity in next-generation (xG) wireless systems. By fusing multimodal sensor data, CT advocates a high-fidelity and low-overhead CA paradigm, which is promising to provide accurate channel prediction in cross-domain and high-mobility scenarios of ubiquitous xG networks. However, existing 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 scene recognition, cooperative sensing, and decentralized training, comprising versatile model configuration, multimodal cooperative sensing, and lightweight twin modeling modules. Additionally, this article presents a detailed concept, technical features, and case studies of CT, outlines mainstream trends of realization methods, followed by potential applications of CT-empowered ubiquitous connectivity, and issues requiring future investigations.