Quality of Service Projects for ME, MTech, Masters, MS abroad, and PhD students. These Quality of Service 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 Quality of Service Projects
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Bee System-Based Self Configurable Optimized Resource Allocation Technique in Device-to- Device (D2D) Communication Networks
This project focuses on improving mobile network performance as traffic keeps growing. It uses a method inspired by how bees search for food to efficiently allocate resources between nearby devices. If the network is overloaded, extra resources and relay nodes are added to maintain good service. The approach improves user experience by reducing delay, saving energy, and supporting mobility. -
Multi–Operator Spectrum and MEC Resource Sharing in Next Generation Cellular Networks
This project focuses on improving the performance of next-generation cellular networks. It allows multiple network operators to share spectrum and computing resources at the network edge. Users get faster and more reliable services by moving their network functions to less busy locations. Tests show it reduces delays, increases data speed, and makes better use of network resources. -
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. -
Joint RIS-Aided Precoding and Multislot Scheduling for Maximum User Admission in Smart Cities
This project focuses on improving wireless networks in smart cities using special surfaces called reconfigurable intelligent surfaces (RIS). These surfaces can control how signals travel, helping more users get better service. The study develops a method to schedule users and adjust signals efficiently, even when the system is complex. Tests show this approach serves more users and works well even when network information is slightly outdated. -
Massive MIMO for Serving Federated Learning and Non-Federated Learning Users
This project focuses on improving future wireless networks that serve two types of users at the same time. It uses federated learning to keep user data private while sending and receiving information efficiently. The study compares two communication methods to see which delivers better speed and reliability. The results show that the proposed methods work better than existing ones, especially when using the full-duplex approach. -
Multi-Antenna Coded Caching for Location-Dependent Content Delivery
This project focuses on improving virtual reality experiences over wireless networks. It uses the extra memory in users’ devices to store parts of content, reducing the load on the network. The system decides how much content each user should store based on their location and connection quality. This approach makes content delivery faster and more reliable for everyone. -
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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