The wireless spectrum's increasing complexity poses challenges and opportunities, highlighting the necessity for real-time solutions and robust data processing capabilities. Digital Twin (DT), virtual replicas of physical systems, integrate real-time data to mirror their real-world counterparts, enabling precise monitoring and optimization. Incorporating DTs into wireless communication enhances predictive maintenance, resource allocation, and troubleshooting, thus bolstering network reliability. Our paper introduces TwiNet, enabling bidirectional, near-realtime links between real-world wireless spectrum scenarios and DT replicas. Utilizing the protocol, MQTT, we can achieve data transfer times with an average latency of 14 ms, suitable for real-time communication. This is confirmed by monitoring real-world traffic and mirroring it in real-time within the DT's wireless environment. We evaluate TwiNet's performance in two use cases: (i) assessing risky traffic configurations of UEs in a Safe Adaptive Data Rate (SADR) system, improving network performance by approximately 15% compared to original network selections; and (ii) deploying new CNNs in response to jammed pilots, achieving up to 97% accuracy training on artificial data and deploying a new model in as low as 2 minutes to counter persistent adversaries. TwiNet enables swift deployment and adaptation of DTs, addressing crucial challenges in modern wireless communication systems.

翻译：无线频谱日益增长的复杂性带来了挑战与机遇，突显了实时解决方案和强大数据处理能力的必要性。数字孪生（DT）作为物理系统的虚拟副本，通过整合实时数据来映射其实体对应物，从而实现精确监控与优化。将DT融入无线通信可增强预测性维护、资源分配和故障排除能力，进而提升网络可靠性。本文提出TwiNet，它能够在现实世界无线频谱场景与DT副本之间建立双向、近实时的连接链路。利用MQTT协议，我们实现了平均延迟为14毫秒的数据传输，满足实时通信需求。这一性能通过监控现实流量并在DT无线环境中实时镜像得到验证。我们在两个应用场景中评估了TwiNet的性能：（i）在安全自适应数据速率（SADR）系统中评估用户设备（UE）的风险流量配置，相较于原始网络选择方案，网络性能提升约15%；（ii）针对受干扰导频部署新的卷积神经网络（CNN），在人工数据上训练达到97%的准确率，并能在低至2分钟内部署新模型以应对持续对抗攻击。TwiNet实现了DT的快速部署与自适应，为现代无线通信系统的关键挑战提供了解决方案。