Non-Orthogonal Multiple Access Projects for ME, MTech, Masters, MS abroad, and PhD students. These Non-Orthogonal Multiple Access 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 Non-Orthogonal Multiple Access Projects
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Computation Rate Maximization for Wireless-Powered Edge Computing With Multi-User Cooperation
This project studies a system where small devices can share computing tasks and get energy wirelessly. Devices work together in groups to split tasks between themselves and a central hub. The goal is to finish more computing work faster while saving energy. The team developed algorithms, including one using deep learning, to make this process efficient and quick. -
A Generalized Delay and Backlog Analysis for Multiplexing URLLC and eMBB: Reconfigurable Intelligent Surfaces or Decode-and-Forward
This project studies how to improve communication networks that carry both urgent and high-data traffic. It looks at using smart surfaces and relay nodes to strengthen signals and create multiple paths. The research predicts delays and data backlogs using a new mathematical approach. Results show this method gives more accurate performance estimates than older techniques. -
Double-Faced Active Intelligent Reflecting Surfaces-Assisted Symbiotic Radio Communications
This project focuses on improving energy efficiency for Internet-of-Things devices in next-generation wireless networks. It uses a special system called symbiotic radio with a double-sided intelligent surface to help devices send and receive signals more efficiently. The researchers designed an algorithm to optimize the system so it works well while using less power. Simulations show it performs better than other similar methods. -
Estimation of Interference Correlation in mmWave Cellular Systems
This project studies how signals from multiple devices interfere with a base station in a cellular network. The researchers focus on estimating this interference using the signals received by the base station. They use a special method that takes advantage of the way signals reflect in millimeter-wave frequencies to improve accuracy. The approach also reduces errors when dealing with large amounts of data. -
Optimal RIS Partitioning and Power Control for Bidirectional NOMA Networks
This project studies how special smart surfaces, called RIS, can improve wireless communication in NOMA networks. The method divides the surface to make signals stronger for users and removes the need for adjusting uplink power. It tests different scenarios to ensure good quality and fair data rates. Simulations show that using RIS greatly improves network performance. -
Relay-Aided Uplink NOMA Under Non-Orthogonal CCI and Imperfect SIC in 6G Networks
This project studies how future 6G networks can handle many users and machines sharing the same resources. It looks at using NOMA and relays to improve communication even when interference is high. The work analyzes how nearby transmissions reduce performance and proposes ways to optimize power and relay placement. Simulations show that these optimizations can make the network more reliable. -
Reliability in Post-Disaster Networks A Novel Interference-Mitigation Strategy
This project studies how to keep mobile networks working well after a disaster. It looks at how active and inactive base stations affect signal quality for users. The study proposes a way to improve coverage and reliability by selectively silencing some base stations. The results show that this method can help users stay connected even in emergency situations. -
Towards 6G V2X Sidelink: Survey of Resource Allocation—Mathematical Formulations, Challenges, and Proposed Solutions
This project studies how future 6G networks can improve communication between vehicles and other devices on the road. It looks at ways to share network resources fairly and efficiently while avoiding interference and collisions. The study also explores new techniques like machine learning and edge computing to make vehicle communication faster and more reliable. Overall, it aims to make connected vehicles safer and more efficient in a hyperconnected 6G environment.
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