Digital Twin Backed Closed-Loops for Energy-Aware and Open RAN-based Fixed Wireless Access Serving Rural Areas

TL;DR

This paper introduces a Digital Twin-assisted closed-loop system for energy-efficient, open RAN-based Fixed Wireless Access in rural areas, optimizing resource management to improve rural internet access cost-effectively.

Contribution

It proposes a novel Digital Twin framework for automating energy-aware resource management in open RAN FWA systems serving rural areas, integrating reinforcement learning and convex optimization.

Findings

Efficient utilization of radio and energy resources demonstrated.

The approach reduces energy costs while satisfying slice requirements.

Digital Twin effectively replicates physical network behavior for optimization.

Abstract

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.