Internet access in rural areas should be improved to support digital inclusion and 5G services. Due to the high deployment costs of fiber optics in these areas, Fixed Wireless Access (FWA) has become a preferable alternative. Additionally, the Open Radio Access Network (O-RAN) can facilitate the interoperability of FWA elements, allowing some FWA functions to be deployed at the edge cloud. However, deploying edge clouds in rural areas can increase network and energy costs. To address these challenges, we propose a closed-loop system assisted by a Digital Twin (DT) to automate energy-aware O-RAN based FWA resource management in rural areas. We consider the FWA and edge cloud as the Physical Twin (PT) and design a closed-loop that distributes radio resources to edge cloud instances for scheduling. We develop another closed-loop for intra-slice resource allocation to houses. We design an energy model that integrates radio resource allocation and formulate ultra-small and small-timescale optimizations for the PT to maximize slice requirement satisfaction while minimizing energy costs. We then design a reinforcement learning approach and successive convex approximation to address the formulated problems. We present a DT that replicates the PT by incorporating solution experiences into future states. The results show that our approach efficiently uses radio and energy resources.

翻译：为支持数字包容与5G服务，农村地区的互联网接入亟待改善。由于光纤在这些区域部署成本高昂，固定无线接入已成为更优替代方案。此外，开放无线接入网络可促进FWA组件的互操作性，使部分FWA功能能够部署在边缘云。然而，在农村地区部署边缘云可能增加网络与能源成本。为应对这些挑战，我们提出一种数字孪生辅助的闭环系统，用于实现农村地区基于O-RAN的能源感知型FWA资源管理自动化。我们将FWA与边缘云视为物理孪生体，并设计了一个将无线资源分配给边缘云实例进行调度的闭环系统。同时开发了另一个用于住宅内切片资源分配的闭环系统。我们构建了整合无线资源分配的能源模型，并为物理孪生体制定了超小时间尺度与小时时间尺度的优化方案，以在最小化能源成本的同时最大化切片需求满足度。随后设计了强化学习方法与逐次凸逼近算法来解决所构建的优化问题。我们提出的数字孪生体通过将解决方案经验融入未来状态来复现物理孪生体。结果表明，我们的方法能高效利用无线资源与能源资源。