Network Slicing Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Network Slicing ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Network Slicing Projects
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Fast Context Adaptation in Cost-Aware Continual Learning
This project studies how smart computer programs can manage resources in 5G networks. It looks at a problem where learning these strategies can use up the network’s resources and affect users. The researchers propose a method that lets the program learn quickly while using very few resources. Their approach adapts to changes and keeps the user experience smooth. -
Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies
This project focuses on making wireless networks smarter and more secure. It uses artificial intelligence to manage network resources for many users and base stations. The system also studies how a jammer can disrupt the network and how to defend against it. The methods are tested in simulations to show they work well. -
ATHENA: An Intelligent Multi-x Cloud Native Network Operator
This project develops Athena, a new system for managing modern mobile networks like 4G and 5G. It makes networks more flexible, efficient, and easy to control across different vendors and devices. Athena reduces operational overhead, saves energy, and keeps the network highly reliable. The system was tested and shown to improve speed, performance, and sustainability. -
Cloud-native orchestration framework for network slice federation across administrative domains in 5G/6G mobile networks
This project focuses on improving mobile networks for connected and automated vehicles. It ensures that users keep a smooth connection even when moving between different network operators. The researchers designed a system that allows mobile operators to share network resources efficiently. They tested it on a 5G platform and studied how different strategies affect performance. -
A QoS Improving Downlink Scheduling Scheme for Slicing in 5G Radio Access Network (RAN)
This project focuses on improving 5G networks. It looks at how to share radio resources fairly among different services. The method ensures each service meets its quality targets. Tests show it works better than existing approaches in efficiency and reliability. -
An eXtended Reality Offloading IP Traffic Dataset and Models
This project focuses on making extended reality (XR) devices lighter and more comfortable by using 5G networks to handle heavy processing remotely. The researchers created a new dataset showing how XR apps use network resources in demanding scenarios. They also developed models to simulate this network traffic and tested them using a 5G emulator. The work helps students and engineers design and improve XR systems more effectively. -
Performance Assessment of an ITU-T Compliant Machine Learning Enhancements for 5G RAN Network Slicing
This project focuses on improving how 5G networks share resources among multiple users. It introduces a way to give priority to different network slices so each gets fair performance. The study uses machine learning to speed up decisions about resource allocation. The results show that these methods work fast and accurately, but extra checks are needed when network conditions change or traffic is high. -
RAN Slicing with Inter-Cell Interference Control and Link Adaptation for Reliable Wireless Communications
This project focuses on improving 5G networks to handle two types of data traffic: one that needs very fast and reliable delivery, and another that carries large amounts of data. It proposes a new method to manage interference and resources without needing complex coordination between cells. The approach ensures reliable delivery and low delays for urgent data while using the network efficiently for high-data traffic. Simulations show it works better than existing methods.
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