5G Networks Projects for ME, MTech, Masters, MS abroad, and PhD students. These 5G Networks ieee projects are implemented with future work and extension for final year students with research paper writing and 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 5G Networks Projects
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Design of Computing-Aware Traffic Steering Architecture for 5G Mobile User Plane
This project focuses on improving 5G networks to make them faster and more reliable at the edge of the network. It studies how to smartly direct traffic between multiple service locations so performance does not drop. The researchers propose new ways to upgrade 5G systems and tested their methods. Their solution made service connections 30% to 50% faster than current standard methods. -
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. -
RF Energy Harvesting Techniques for Battery-Less Wireless Sensing Industry 4.0 and Internet of Things A Review
This project focuses on using energy from the environment to power small IoT devices without batteries. It studies different ways to harvest energy, especially from radio waves, to run low-power electronics. The goal is to make devices that need no maintenance and work in smart industries, agriculture, healthcare, and 5G networks. It also looks at future trends in improving these systems and integrating them into modern technology. -
Congestion Control Prediction Model for 5G Environment Based on Supervised and Unsupervised Machine Learning Approach
This project studies how to reduce network congestion in 5G systems using machine learning. It compares many algorithms to find which ones best predict where congestion will happen. The researchers tested both supervised and unsupervised learning methods. In the end, they selected the five best-performing algorithms for accurate congestion prediction.
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