NWDAF in 5G Network Research Topics

NWDAF in 5G Network Research Topics is the topic here we proposed in this research. NWDAF is the expansion of Network Data Analytics Function. This process will effectively distribute and optimize the resources on the basis of actual-time NWDAF. In this we provide the details or concepts that are relevant to this proposed technique.

  1. Define Resource Scaling using NWDAF in 5G Network

Initially we begin with the definition for this proposed Resource Scaling using NWDAF in a 5G network. It refers to the optimization and dynamic adjustment of network resources on the basis of actual-time analysis and understandings that are offered by NWDAF. This procedure intelligently distributes and supplies resources like bandwidth, storage and computing power to come across different challenges and makes sure optimal achievements over the 5G network ecosystem.

  1. What is Resource Scaling using NWDAF in 5G Network?

Afterwards we look for the brief explanation for this proposed technique. Resource scaling in a 5G network defines the procedure of dynamically modifying and optimizing network resources to effectively adapt different difficulties, service requirements and traffic patterns over the context of a 5G telecommunications framework. This contains handling and distributing resources like network bandwidth, storage capacity, radio spectrum and computing resources (e.g., CPU, memory) in reply to alter situations and challenges over the network.

  1. Where Resource Scaling using NWDAF in 5G Network used?

Next to the brief explanation we interpret where to utilize this proposed technique. Resource scaling methods are utilized in different industries and fields where effective optimization of resources and management are important. Several common regions where the resource scaling methods are utilized are: Internet of Things, Cloud Computing, Virtualization and Containerization, telecommunications and networking, Web application and service, data centers and machine learning and AI.

  1. Why Resource Scaling using NWDAF in 5G Network technology proposed? , previous technology issues

In this research we proposed the resource scaling using NWDAF in a 5G network to distribute and handle the resources in a dynamic network environment, existing methods come across important difficulties, like mobility issues, real-time data gathering, model complexity and missing data handling. This problem will possibly cause an accurate forecasting and will decrease the resource allocation achievement. Some of the major problems are: Decrease performance, Real-time data collection, overloading and Noise and Overfitting.

  1. Algorithms / protocols

We have to utilize some of the methods for our proposed research to overcome the problems in the existing technologies. The methods that we employed are as follows: Hybrid Diffusion-GRU Hidden Markov Model (HDGHMM), Probabilistic Reinforcement Genetic Allocation (PROGA) and Median Min-Max Scaling with Recursive Feature Elimination (M3S-RFE).

  1. Comparative study / Analysis

Our proposed NWDAF technique uses some methods to analyze the performance of this proposed technique. The methods that we compared are:

  • In this we propose a M3S-RFE technique to process the gathered data.
  • To enhance the effectiveness of the forecasting model in this research we propose a HDGHMM technique.
  • The 5G network will be efficiently handled with the utilization of a proposed novel PROGA method.
  1. Simulation results / Parameters

For this proposed technique we compared different performance metrics or parameters for this research to find the best result for this research. The metrics that we contrasted are: Accuracy (%) and loss with Epoch and True positive rate with False positive rate and the Throughput (MB/s) and CPU utilization (%) with time (m/s).

  1. Dataset LINKS / Important URL

The below are some of the crucial links that are utilized to overview the concepts of this proposed NWDAF system in 5G network technology. These are useful to clarify the doubts.

  1. Resource Scaling using NWDAF in 5G Network Applications

Let’s see the applications to be utilized for this proposed resource scaling using NWDAF in the 5G network which has some crucial applications among different fields. It ensures improved service consistency, assists traffic steering and load balancing, effective resource utilization, permitting edge computing efficiency and provides network slicing optimization by driving the evolution of intelligent and adaptive 5G framework. By examining the real-time data, NWDAF improves the Quality of Service (QoS) for the applications by distributing the resources on the basis of application demands, network traffic and user behavior.

  1. Topology for Resource Scaling using NWDAF in 5G Network

The topology for our proposed research will generally contain a hierarchical structure that contains Core Network (CN) and the Radio Access Network (RAN) components. The core network will consists of the functions like SMF, NWDAF and the AMF, which are linked through service-based interfaces. Radio access is offered by gNBs (base stations) interacting with the core network. Deep learning models for resource scaling will be arranged at the edge nodes or cloud servers over this topology to optimize network achievement and efficacy.

  1. Environment for Resource Scaling using NWDAF in 5G Network

Here the environment for this proposed technique contains actual-time data gathering from network elements. Then the data is pre-processed and then provided into deep learning models that are presented on edge or cloud servers. The models then examine the network environment and forecast resource demands, allowing optimization and resource allocation. The feedback loops will ensure the constant enhancement of resource scaling decisions on the basis of actual network achievement.

  1. Simulation tools

Now we see the simulation tool or software requirements that are needed for the proposed NWDAF in the 5G network. The NS 3.26 with python developmental tool is utilized to implement our research. Then the operating system that used to execute our work is Ubuntu 16.04 LTS.

  1. Results

The NWDAF technology is proposed in this research and it overcomes some of the issues in the existing technology. This technology is compared with different methods and then is contrasted with various performance metrics to get the accurate possible findings. Then the research is implemented by using the tool NS3.26 with python.

NWDAF in 5G Network Research Ideas:

The succeeding are the topics that we offer on the basis of this proposed NWDAF in 5G technology; we go through the topics to know the details about our proposed technique and such other relevant Information

  1. Towards Supporting Intelligence in 5G/6G Core Networks: NWDAF Implementation and Initial Analysis
  2. Disaggregated Near-RT RIC Control Plane with Unified 5G DB for NS, MEC and NWDAF Integration
  3. NWDAF UDI (Use-case Development Interface) for End-to-end AI Enabled 5G and Beyond Networks
  4. Data Collection Using NWDAF Network Function in a 5G Core Network with Real Traffic
  5. Poster: Exploring Synthetic Data Generation for Anomaly Detection in the 5G NWDAF Architecture
  6. An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization
  7. Machine Learning and Deep Learning Algorithms for Network Data Analytics Function in 5G Cellular Networks
  8. An Open-Source Prototype of Network Data Analytics Function for Next-Generation 5/6G Environments
  9. A Decentralized Collaborative Learning Approach in 5G+ Core Networks
  10. Slice-Level Performance Metric Forecasting in Intelligent Transportation Systems and the Internet of Vehicles
  11. Analytics support in the 5G Core Network for data-driven management of a supplementary backhaul
  12. Blockchain based Data Sharing for User Experience Driven Slice SLA Guarantee
  13. Standardization and technology trends of artificial intelligence for mobile systems
  14. Failure-Aware and Automated Disaster Backup in the 5G Core Network
  15. Privacy-Preserving User Abnormal Behavior Detection in 5G Networks
  16. Intent-Based Network Management in 6G Core Networks
  17. Distributed Intelligence Analysis Architecture for 6G Core Network
  18. Network Automation and Data Analytics in 3GPP 5G Systems
  19. A Method to Improve the Performance of Network Data Analytics Function Based on Transfer Learning
  20. A Survey of 3GPP Release 18 on Network Data Analytics Function Management
  21. Leveraging Network Data Analytics Function and Machine Learning for Data Collection, Resource Optimization, Security and Privacy in 6G Networks
  22. Hierarchical Network Data Analytics Framework for 6G Network Automation: Design and Implementation
  23. An Implementation Study of Network Data Analytic Function in 5G
  24. Machine Learning and 5G Charging Function with Network Analytics Function for Network Slice as a Service
  25. An Evaluation of Intelligent Network Data Analytics Based on Machine Learning In 5G Data Networks
  26. Meta-NWDAF: A Meta-Learning based Network Data Analytic Function for Internet Traffic Prediction
  27. Securing Federated Learning Enabled NWDAF Architecture With Partial Homomorphic Encryption
  28. Combining Network Data Analytics Function and Machine Learning for Abnormal Traffic Detection in Beyond 5G
  29. Ensemble Learning-based Network Data Analytics for Network Slice Orchestration and Management: An Intent-Based Networking Mechanism
  30. C-NWDAF: Designing a Cloud-based Multi-Model Architecture for Network Data Analytics Function
  31. Research on threat modeling for 5G network data analytics function
  32. Design and Implementation of Network Data Analytics Function in 5G
  33. 5G Close Loop Proactive Optimization Using Network Data Analysis Function
  34. REMS: Resource-Efficient and Adaptive Model Selection in 5G NWDAF
  35. A Distributed NWDAF Architecture for Federated Learning in 5G
  36. Insight of Anomaly Detection with NWDAF in 5G
  37. NWDAF in 3GPP 5G Advanced: A Survey
  38. Network Data Analytics Function for IBN-based Network Slice Lifecycle Management
  39. Federated Learning for Distributed NWDAF Architecture
  40. The Challenge of Zero Touch and Explainable AI
  41. Distributed AI-native Architecture for 6G Networks
  42. AI-Driven Data Analytics and Intent-Based Networking for Orchestration and Control of B5G Consumer Electronics Services
  43. A Model Drift Detection and Adaptation Framework for 5G Core Networks
  44. Decentralized Machine Learning based Network Data Analytics for Cognitive Management of Mobile Communication Networks
  45. Location-Privacy Leakage and Integrated Solutions for 5G Cellular Networks and Beyond
  46. Supporting Intelligence in Disaggregated Open Radio Access Networks: Architectural Principles, AI/ML Workflow, and Use Cases
  47. iPaaS: Intelligent Paging as a Service
  48. Perspectives on 6G wireless communications
  49. Deep Learning-Based Symptomizing Cyber Threats Using Adaptive 5G Shared Slice Security Approaches
  50. Service-based Analytics for 5G open experimentation platforms