Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need

• URL: http://arxiv.org/abs/2406.16929v1
• Date: Thu, 13 Jun 2024 06:02:15 GMT
• Title: Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need
• Authors: Tingwei Chen, Yantao Wang, Hanzhi Chen, Zijian Zhao, Xinhao Li, Nicola Piovesan, Guangxu Zhu, Qingjiang Shi,
• Abstract summary: This paper proposes a novel 5G base stations energy consumption modelling method by learning from a real-world dataset used in the ITU 5G Base Station Energy Consumption Modelling Challenge. Our method demonstrates significant improvements over existing models, reducing Mean Absolute Percentage Error (MAPE) from 12.75% to 4.98%, leading to a performance gain of more than 60%.
• Score: 21.483076846188837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The introduction of fifth-generation (5G) radio technology has revolutionized communications, bringing unprecedented automation, capacity, connectivity, and ultra-fast, reliable communications. However, this technological leap comes with a substantial increase in energy consumption, presenting a significant challenge. To improve the energy efficiency of 5G networks, it is imperative to develop sophisticated models that accurately reflect the influence of base station (BS) attributes and operational conditions on energy usage.Importantly, addressing the complexity and interdependencies of these diverse features is particularly challenging, both in terms of data processing and model architecture design. This paper proposes a novel 5G base stations energy consumption modelling method by learning from a real-world dataset used in the ITU 5G Base Station Energy Consumption Modelling Challenge in which our model ranked second. Unlike existing methods that omit the Base Station Identifier (BSID) information and thus fail to capture the unique energy fingerprint in different base stations, we incorporate the BSID into the input features and encoding it with an embedding layer for precise representation. Additionally, we introduce a novel masked training method alongside an attention mechanism to further boost the model's generalization capabilities and accuracy. After evaluation, our method demonstrates significant improvements over existing models, reducing Mean Absolute Percentage Error (MAPE) from 12.75% to 4.98%, leading to a performance gain of more than 60%.

Related papers

• Revisiting DNN Training for Intermittently Powered Energy Harvesting Micro Computers [0.6721767679705013]
  This study introduces and evaluates a novel training methodology tailored for Deep Neural Networks in energy-constrained environments. We propose a dynamic dropout technique that adapts to both the architecture of the device and the variability in energy availability. Preliminary results demonstrate that this strategy provides 6 to 22 percent accuracy improvements compared to the state of the art with less than 5 percent additional compute.
  arXiv  Detail & Related papers  (2024-08-25T01:13:00Z)
• Towards Energy-Aware Federated Traffic Prediction for Cellular Networks [2.360352205004026]
  We propose a novel sustainability indicator that allows assessing the feasibility of machine learning (ML) models. We evaluate state-of-the-art deep learning (DL) architectures in a federated scenario using real-world measurements from base station (BS) sites in the area of Barcelona, Spain. Our findings indicate that larger ML models achieve marginally improved performance but have a significant environmental impact in terms of carbon footprint.
  arXiv  Detail & Related papers  (2023-09-19T14:28:09Z)
• Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning [60.17407932691429]
  Open Radio Access Network systems, with their base stations (vBSs), offer operators the benefits of increased flexibility, reduced costs, vendor diversity, and interoperability. We propose an online learning algorithm that balances the effective throughput and vBS energy consumption, even under unforeseeable and "challenging'' environments. We prove the proposed solutions achieve sub-linear regret, providing zero average optimality gap even in challenging environments.
  arXiv  Detail & Related papers  (2023-09-04T17:30:21Z)
• Power Consumption Modeling of 5G Multi-Carrier Base Stations: A Machine Learning Approach [6.74575019261951]
  We present a power consumption model for 5G AAUs based on artificial neural networks. We show the advantage of designing a general model able to capture the power consumption behaviors of different types of AAUs.
  arXiv  Detail & Related papers  (2022-12-08T15:16:44Z)
• Machine Learning and Analytical Power Consumption Models for 5G Base Stations [6.287715553901191]
  We propose a novel model for a realistic characterisation of the power consumption of 5G multi-carrier BSs. We exploit the knowledge gathered by this framework to derive a realistic and analytically tractable power consumption model.
  arXiv  Detail & Related papers  (2022-09-23T14:07:36Z)
• Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications [64.1076645382049]
  Combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency. In this paper, we study the spectrum, energy, and time resource management for deterministic-CR-NOMA IoT systems.
  arXiv  Detail & Related papers  (2021-09-17T08:55:48Z)
• Power Modeling for Effective Datacenter Planning and Compute Management [53.41102502425513]
  We discuss two classes of statistical power models designed and validated to be accurate, simple, interpretable and applicable to all hardware configurations and workloads. We demonstrate that the proposed statistical modeling techniques, while simple and scalable, predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse Power Distribution Units (more than 2000) using only 4 features.
  arXiv  Detail & Related papers  (2021-03-22T21:22:51Z)
• True-data Testbed for 5G/B5G Intelligent Network [46.09035008165811]
  We build the world's first true-data testbed for 5G/B5G intelligent network (TTIN) TTIN comprises 5G/B5G on-site experimental networks, data acquisition & data warehouse, and AI engine & network optimization. This paper elaborates on the system architecture and module design of TTIN.
  arXiv  Detail & Related papers  (2020-11-26T06:42:36Z)
• Energy-Based Processes for Exchangeable Data [109.04978766553612]
  We introduce Energy-Based Processes (EBPs) to extend energy based models to exchangeable data. A key advantage of EBPs is the ability to express more flexible distributions over sets without restricting their cardinality. We develop an efficient training procedure for EBPs that demonstrates state-of-the-art performance on a variety of tasks.
  arXiv  Detail & Related papers  (2020-03-17T04:26:02Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.