Large Model-Driven Digital Twin Networks for Transmission Optimization in 5G/6G Wireless Communication Systems

Abstract

The next generation of Wireless Communication Networks (WCNs), such as 5G and 6G, require highly adaptive, intelligent, and efficient transmission mechanisms to meet the demands of low latency, high throughput, and robust Quality of Experience (QoE). This paper introduces a novel framework that integrates Large Models (LMs), particularly transformer-based deep learning architectures, with Digital Twin Networks (DTNs) for predictive and real-time optimization in WCNs. The proposed LM-enhanced DTN architecture enables advanced capabilities such as traffic classification, predictive scheduling, quality-aware transmission, and failure forecasting. Experimental evaluations using real-world telemetry datasets demonstrate the superiority of the LM-powered system in achieving over 98% classification accuracy and enhancing 12.4% improvement in QoE in congested scenarios. Additionally, a case study in industrial networks illustrates the effectiveness of this approach in predictive maintenance and adaptive traffic management. This work paves the way for self-optimizing, intelligent wireless networks by harnessing the cognitive power of large AI models in virtual network replicas.