Sixth-generation (6G) is envisioned to be characterized by ubiquitous connectivity, extremely low latency, and enhanced edge intelligence. However, enriching 6G with these features requires addressing new, unique, and complex challenges specifically at the edge of the network. In this article, we propose a wireless digital twin edge network model by integrating digital twin with edge networks to enable new functionalities, such as hyper-connected experience and low-latency edge computing. To efficiently construct and maintain digital twins in the wireless digital twin network, we formulate the edge association problem with respect to the dynamic network states and varying network topology. Furthermore, according to the different running stages, we decompose the problem into two subproblems, including digital twin placement and digital twin migration. Moreover, we develop a deep reinforcement learning (DRL)-based algorithm to find the optimal solution to the digital twin placement problem, and then use transfer learning to solve the digital twin migration problem. Numerical results show that the proposed scheme provides reduced system cost and enhanced convergence rate for dynamic network states.

中文翻译：

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6G 无线数字孪生网络的自适应边缘协会

设想第六代 (6G) 的特点是无处不在的连接性、极低的延迟和增强的边缘智能。然而，通过这些功能丰富 6G 需要解决新的、独特的和复杂的挑战，特别是在网络边缘。在本文中，我们提出了一种无线数字孪生边缘网络模型，通过将数字孪生与边缘网络集成来实现新功能，例如超连接体验和低延迟边缘计算。为了在无线数字孪生网络中有效地构建和维护数字孪生，我们针对动态网络状态和变化的网络拓扑制定了边缘关联问题。此外，根据不同的运行阶段，我们将问题分解为两个子问题，包括数字孪生放置和数字孪生迁移。此外，我们开发了一种基于深度强化学习 (DRL) 的算法来寻找数字孪生放置问题的最佳解决方案，然后使用迁移学习来解决数字孪生迁移问题。数值结果表明，所提出的方案降低了系统成本，并提高了动态网络状态的收敛速度。