Towards Network Data Analytics in 5G Systems and Beyond
- URL: http://arxiv.org/abs/2506.04860v1
- Date: Thu, 05 Jun 2025 10:26:53 GMT
- Title: Towards Network Data Analytics in 5G Systems and Beyond
- Authors: Marcos Lima Romero, Ricardo Suyama,
- Abstract summary: Data has become a critical asset in the digital economy, yet it remains underutilized by Mobile Network Operators (MNOs)<n>This study analyzes trends and gaps in more than 70 articles and proposes two novel use cases to promote the adoption of NWDAF and explore its potential for monetization.
- Score: 0.6906005491572401
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Data has become a critical asset in the digital economy, yet it remains underutilized by Mobile Network Operators (MNOs), unlike Over-the-Top (OTT) players that lead global market valuations. To move beyond the commoditization of connectivity and deliver greater value to customers, data analytics emerges as a strategic enabler. Using data efficiently is essential for unlocking new service opportunities, optimizing operational efficiency, and mitigating operational and business risks. Since Release 15, the 3rd Generation Partnership Project (3GPP) has introduced the Network Data Analytics Function (NWDAF) to provide powerful insights and predictions using data collected across mobile networks, supporting both user-centric and network-oriented use cases. However, academic research has largely focused on a limited set of methods and use cases, driven by the availability of datasets, restricting broader exploration. This study analyzes trends and gaps in more than 70 articles and proposes two novel use cases to promote the adoption of NWDAF and explore its potential for monetization.
Related papers
- Optimizing the Privacy-Utility Balance using Synthetic Data and Configurable Perturbation Pipelines [0.0]
This paper explores the strategic use of modern synthetic data generation and advanced data perturbation techniques to enhance security, maintain analytical utility, and improve operational efficiency when managing large datasets.<n>The goal is to create realistic, privacy-preserving datasets that retain high utility for complex machine learning tasks and analytics, a critical need in the data-sensitive industries like BFSI, Healthcare, Retail, and Telecommunications.
arXiv Detail & Related papers (2025-04-24T15:52:53Z) - Promoting User Data Autonomy During the Dissolution of a Monopolistic Firm [5.864623711097197]
We show how the framework of Conscious Data Contribution can enable user autonomy during under dissolution.
We explore how fine-tuning and the phenomenon of "catastrophic forgetting" could actually prove beneficial as a type of machine unlearning.
arXiv Detail & Related papers (2024-11-20T18:55:51Z) - MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services [94.61039892220037]
We propose an immersion-aware model trading framework that facilitates data provision for services while ensuring privacy through federated learning (FL)<n>We design an incentive mechanism to incentivize metaverse users (MUs) to contribute high-value models under resource constraints.<n>We develop a fully distributed dynamic reward algorithm based on deep reinforcement learning, without accessing any private information about MUs and other MSPs.
arXiv Detail & Related papers (2024-10-25T16:20:46Z) - Decentralized Multimedia Data Sharing in IoV: A Learning-based Equilibrium of Supply and Demand [57.82021900505197]
Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications.
Decentralized data sharing can improve security, privacy, reliability, and facilitate infotainment data sharing in IoVs.
We propose a decentralized data-sharing incentive mechanism based on multi-intelligent reinforcement learning to learn the supply-demand balance in markets.
arXiv Detail & Related papers (2024-03-29T14:58:28Z) - A Bargaining-based Approach for Feature Trading in Vertical Federated
Learning [54.51890573369637]
We propose a bargaining-based feature trading approach in Vertical Federated Learning (VFL) to encourage economically efficient transactions.
Our model incorporates performance gain-based pricing, taking into account the revenue-based optimization objectives of both parties.
arXiv Detail & Related papers (2024-02-23T10:21:07Z) - Enabling Inter-organizational Analytics in Business Networks Through
Meta Machine Learning [0.0]
Fear of disclosing sensitive information as well as the sheer volume of the data that would need to be exchanged are key inhibitors for the creation of effective system-wide solutions.
We propose a meta machine learning method that deals with these obstacles to enable comprehensive analyses within a business network.
arXiv Detail & Related papers (2023-03-28T09:06:28Z) - On Inferring User Socioeconomic Status with Mobility Records [61.0966646857356]
We propose a socioeconomic-aware deep model called DeepSEI.
The DeepSEI model incorporates two networks called deep network and recurrent network.
We conduct extensive experiments on real mobility records data, POI data and house prices data.
arXiv Detail & Related papers (2022-11-15T15:07:45Z) - An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and
Characterization [3.5573601621032935]
This paper focuses on demonstrating a working system prototype of the 5G Core (5GC) network and the Network Data Analytics Function (NWDAF) used to bring the benefits of data-driven techniques to fruition.
Analyses of the network-generated data explore core intra-network interactions through unsupervised learning, clustering, and evaluate these results as insights for future opportunities and works.
arXiv Detail & Related papers (2022-09-21T15:21:59Z) - Augmented Bilinear Network for Incremental Multi-Stock Time-Series
Classification [83.23129279407271]
We propose a method to efficiently retain the knowledge available in a neural network pre-trained on a set of securities.
In our method, the prior knowledge encoded in a pre-trained neural network is maintained by keeping existing connections fixed.
This knowledge is adjusted for the new securities by a set of augmented connections, which are optimized using the new data.
arXiv Detail & Related papers (2022-07-23T18:54:10Z) - Distributed data analytics [8.415530878975751]
Recommendation systems are a key component of online service providers.
Financial industry has adopted ML to harness large volumes of data in areas such as fraud detection, risk-management, and compliance.
arXiv Detail & Related papers (2022-03-26T14:10:51Z) - Provably Efficient Causal Reinforcement Learning with Confounded
Observational Data [135.64775986546505]
We study how to incorporate the dataset (observational data) collected offline, which is often abundantly available in practice, to improve the sample efficiency in the online setting.
We propose the deconfounded optimistic value iteration (DOVI) algorithm, which incorporates the confounded observational data in a provably efficient manner.
arXiv Detail & Related papers (2020-06-22T14:49:33Z)
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.