Renewable Energy Powered and Open RAN-based Architecture for 5G Fixed Wireless Access Provisioning in Rural Areas

TL;DR

This paper introduces a renewable energy-powered, Open RAN-based architecture for 5G fixed wireless access in rural areas, optimizing radio and energy resources through closed-loop control and machine learning to improve efficiency and meet delay requirements.

Contribution

It presents a novel renewable energy and Open RAN integrated architecture with a three-level closed-loop system and optimization methods for resource allocation in rural 5G FWA.

Findings

Achieves 97.14% slice delay budget satisfaction.

Develops a new energy model leveraging renewable sources.

Uses reinforcement learning for resource optimization.

Abstract

Due to the high costs of optical fiber deployment in Low-Density and Rural Areas (LDRAs), 5G Fixed Wireless Access (5G FWA) recently emerged as an affordable solution. A widely adopted deployment scenario of 5G FWA includes edge cloud that supports computing services and Radio Access Network (RAN) functions. Such edge cloud requires network and energy resources for 5G FWA. This paper proposes renewable energy powered and Open RAN-based architecture for 5G FWA serving LDRAs using three-level closed-loops. Open RAN is a new 5G RAN architecture allowing Open Central Unit and Open Distributed Unit to be distributed in virtualized environment. The first closed-loop distributes radio resources to Open RAN instances and slices at the edge cloud. The second closed-loop allocates radio resources to houses. We design a new energy model that leverages renewable energy. We jointly optimize radio and energy resource allocation in closed-loop 3. We formulate ultra-small and small-time scale optimization problems that link closed-loops to maximize communication utility while minimizing energy costs. We propose reinforcement learning and successive convex approximation to solve the formulated problems. Then, we use solution data and continual learning to improve resource allocation on a large timescale. Our proposal satisfies 97.14% slice delay budget.