Data-driven Energy Efficiency Modelling in Large-scale Networks: An Expert Knowledge and ML-based Approach

• URL: http://arxiv.org/abs/2401.00443v2
• Date: Tue, 4 Jun 2024 10:01:55 GMT
• Title: Data-driven Energy Efficiency Modelling in Large-scale Networks: An Expert Knowledge and ML-based Approach
• Authors: David López-Pérez, Antonio De Domenico, Nicola Piovesan, Merouane Debbah,
• Abstract summary: This paper introduces the simulated reality of communication networks (SRCON) framework. It harnesses live network data and employs a blend of machine learning (ML)- and expert-based models. Results show significant gains over a state-of-the art method used by a operator for network energy efficiency modeling.
• Score: 8.326834499339107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The energy consumption of mobile networks poses a critical challenge. Mitigating this concern necessitates the deployment and optimization of network energy-saving solutions, such as carrier shutdown, to dynamically manage network resources. Traditional optimization approaches encounter complexity due to factors like the large number of cells, stochastic traffic, channel variations, and intricate trade-offs. This paper introduces the simulated reality of communication networks (SRCON) framework, a novel, data-driven modeling paradigm that harnesses live network data and employs a blend of machine learning (ML)- and expert-based models. These mix of models accurately characterizes the functioning of network components, and predicts network energy efficiency and user equipment (UE) quality of service for any energy carrier shutdown configuration in a specific network. Distinguishing itself from existing methods, SRCON eliminates the reliance on expensive expert knowledge, drive testing, or incomplete maps for predicting network performance. This paper details the pipeline employed by SRCON to decompose the large network energy efficiency modeling problem into ML and expert-based submodels. It demonstrates how, by embracing stochasticity, and carefully crafting the relationship between such submodels, the overall computational complexity can be reduced and prediction accuracy enhanced. Results derived from real network data underscore the paradigm shift introduced by SRCON, showcasing significant gains over a state-of-the art method used by a operator for network energy efficiency modeling. The reliability of this local, data-driven modeling of the network proves to be a key asset for network energy-saving optimization.

Related papers

• DRL Optimization Trajectory Generation via Wireless Network Intent-Guided Diffusion Models for Optimizing Resource Allocation [58.62766376631344]
  We propose a customized wireless network intent (WNI-G) model to address different state variations of wireless communication networks. Extensive simulation achieves greater stability in spectral efficiency and variations of traditional DRL models in dynamic communication systems.
  arXiv  Detail & Related papers  (2024-10-18T14:04:38Z)
• TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics [14.25304439234864]
  We introduce a novel resilience prediction framework for complex networks, designed to tackle this issue through generative data augmentation of network topology and dynamics. Experiment results on three network datasets demonstrate that our proposed framework TDNetGen can achieve high prediction accuracy up to 85%-95%.
  arXiv  Detail & Related papers  (2024-08-19T09:20:31Z)
• RACH Traffic Prediction in Massive Machine Type Communications [5.416701003120508]
  This paper presents a machine learning-based framework tailored for forecasting bursty traffic in ALOHA networks. We develop a new low-complexity online prediction algorithm that updates the states of the LSTM network by leveraging frequently collected data from the mMTC network. We evaluate the performance of the proposed framework in a network with a single base station and thousands of devices organized into groups with distinct traffic-generating characteristics.
  arXiv  Detail & Related papers  (2024-05-08T17:28:07Z)
• Node Centrality Approximation For Large Networks Based On Inductive Graph Neural Networks [2.4012886591705738]
  Closeness Centrality (CC) and Betweenness Centrality (BC) are crucial metrics in network analysis. Their practical implementation on extensive networks remains computationally demanding due to their high time complexity. We propose the CNCA-IGE model, which is an inductive graph encoder-decoder model designed to rank nodes based on specified CC or BC metrics.
  arXiv  Detail & Related papers  (2024-03-08T01:23:12Z)
• Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control [63.310780486820796]
  We show how a parameterization of recurrent connectivity influences robustness in closed-loop settings. We find that closed-form continuous-time neural networks (CfCs) with fewer parameters can outperform their full-rank, fully-connected counterparts.
  arXiv  Detail & Related papers  (2023-10-05T21:44:18Z)
• Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks [52.96907334080273]
  As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions. We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
  arXiv  Detail & Related papers  (2023-09-16T11:37:23Z)
• Generalization and Estimation Error Bounds for Model-based Neural Networks [78.88759757988761]
  We show that the generalization abilities of model-based networks for sparse recovery outperform those of regular ReLU networks. We derive practical design rules that allow to construct model-based networks with guaranteed high generalization.
  arXiv  Detail & Related papers  (2023-04-19T16:39:44Z)
• AI in 6G: Energy-Efficient Distributed Machine Learning for Multilayer Heterogeneous Networks [7.318997639507269]
  We propose a novel layer-based HetNet architecture which distributes tasks associated with different machine learning approaches across network layers and entities. Such a HetNet boasts multiple access schemes as well as device-to-device (D2D) communications to enhance energy efficiency.
  arXiv  Detail & Related papers  (2022-06-04T22:03:19Z)
• Federated Learning over Wireless IoT Networks with Optimized Communication and Resources [98.18365881575805]
  Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention. It is of interest to investigate fast responding and accurate FL schemes over wireless systems. We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
  arXiv  Detail & Related papers  (2021-10-22T13:25:57Z)
• Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks [0.9809636731336702]
  We propose a data- and knowledge-driven probabilistic graphical model for energy systems based on the framework of graph neural networks (GNNs) The model can explicitly factor in domain knowledge, in the form of grid topology or physics constraints, thus resulting in sparser architectures and much smaller parameters dimensionality. Results obtained from a real-world smart-grid demonstration project show how the GNN was used to inform grid congestion predictions and market bidding services.
  arXiv  Detail & Related papers  (2021-03-12T13:00:01Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)

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.