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

IF 5.3 2区 计算机科学 Q1 TELECOMMUNICATIONS IEEE Transactions on Green Communications and Networking Pub Date : 2024-07-22 DOI:10.1109/TGCN.2024.3431989
Anselme Ndikumana;Kim Khoa Nguyen;Mohamed Cheriet
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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 time scale. Our proposal satisfies 97.14% slice delay budget.
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以可再生能源为动力、基于开放式 RAN 的架构为农村地区提供 5G 固定无线接入服务
由于在低密度和农村地区(LDRA)部署光纤成本高昂,5G 固定无线接入(5G FWA)最近成为一种经济实惠的解决方案。广泛采用的 5G FWA 部署方案包括支持计算服务和无线接入网(RAN)功能的边缘云。这种边缘云需要 5G FWA 的网络和能源资源。本文提出了以可再生能源为动力、基于开放 RAN 的架构,利用三级闭环为服务于 LDRA 的 5G FWA 提供支持。开放式 RAN 是一种全新的 5G RAN 架构,允许在虚拟化环境中分布开放式中央单元和开放式分布单元。第一个闭环将无线电资源分配给边缘云的 Open RAN 实例和片。第二个闭环将无线电资源分配给房屋。我们设计了一种利用可再生能源的新型能源模式。我们在闭环 3 中联合优化无线电和能源资源分配。我们制定了超小型和小时间规模的优化问题,将闭环连接起来,使通信效用最大化,同时使能源成本最小化。我们提出了强化学习和连续凸近似来解决所提出的问题。然后,我们利用求解数据和持续学习来改善大时间尺度上的资源分配。我们的建议能满足 97.14% 的切片延迟预算。
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来源期刊
IEEE Transactions on Green Communications and Networking
IEEE Transactions on Green Communications and Networking Computer Science-Computer Networks and Communications
CiteScore
9.30
自引率
6.20%
发文量
181
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