Wireless Networks Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Wireless Networks 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 Wireless Networks Projects
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Deep Learning for Radio Resource Allocation Under DoS Attack
This project develops an intelligent system that helps wireless networks stay secure and efficient even under cyberattacks. It uses deep reinforcement learning to manage how sensors send data and save energy while resisting denial-of-service attacks. The system can also detect when attackers change their strategy and quickly adapt to it. This makes the network more reliable and resilient in real-time conditions. -
Learning Random Access Schemes for Massive Machine-Type Communication With MARL
This project studies how multiple smart devices can share a communication network efficiently without needing complex coordination. It uses learning techniques so devices can decide when to send data on their own. The methods improve network performance and fairness while working well for many low-power devices. Simulations show the approach adapts to changing traffic and works even as more devices join the network. -
Cloud Radio Random-Access Networks With Multi-Packet Reception Capability
This project studies a new way to manage mobile networks using cloud computing. It moves some base station functions to the cloud, which reduces hardware and costs. The researchers developed a method to improve data transmission and measured its performance. Their results show that this approach can make networks faster and more efficient. -
Advancing UAV Communications: A Comprehensive Survey of CuttingEdge Machine Learning Techniques
This project studies how machine learning can help drones communicate better and work smarter in mobile networks. It explains how drones can act as flying users or base stations to improve network coverage. The paper reviews different learning methods that help drones save energy and find the best flight paths. It also explores how new AI techniques can connect with cloud and edge systems for better performance. -
Artificial Intelligence-Defined Wireless Networking for Computational Offloading and Resource Allocation in Edge Computing Networks
This project focuses on improving how data and computing resources are managed in next-generation 5G networks. It brings computing closer to mobile users so applications run faster and more reliably. The researchers developed an AI-based system that decides how to share resources and offload tasks in real time. Their approach can serve more users efficiently, even in busy and fast-changing network conditions. -
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. -
Federated Learning in Heterogeneous Wireless Networks With Adaptive Mixing Aggregation and Computation Reduction
This project improves federated learning for devices with different computing powers and network conditions. It uses a new framework called AMA-FES to make training more stable and accurate. Low-power devices only update part of the model to save computation. The system is tested with drones doing image classification and shows better results without extra cost. -
A Min-Max Optimization-Based Approach for Secure Localization in Wireless Networks
This project focuses on finding the location of a device in a wireless network even when some devices try to give false information. Unlike existing methods, it assumes all devices might be dishonest at first. The researchers developed two new techniques to handle the worst-case attacks and tested them using computer simulations. Their methods were more reliable and secure than previous approaches. -
Cell-Free UAV Networks With Wireless Fronthaul: Analysis and Optimization
This project studies how drones can be used to improve wireless networks without relying on traditional cell towers. Users send data to drones, which then forward it to a central processing point using wireless links. The work looks at different ways to share these links and optimizes drone positions and power settings to get the best network performance. The main benefit comes from placing the drones in the right 3D locations. -
Enabling Cross-Technology Coexistence for ZigBee Devices Through Payload Encoding
This project focuses on improving communication between low-power devices and WiFi networks that share the same frequency. The researchers created a system called SledZig that allows these devices to send more data without changing existing standards. SledZig reduces interference from WiFi while keeping WiFi speed almost the same. Experiments show it helps low-power devices communicate better with minimal impact on WiFi. -
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. -
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. -
Optimizing D2D Communication With Practical STAR-RIS and Irregular Configurations
This project improves device-to-device wireless communication using smart surfaces that can send and reflect signals at the same time. It makes the system cheaper and able to cover more area by using simple signal controllers. The research also proposes turning on only some surface elements in a smart way to improve performance. Their method boosts both the communication speed and energy efficiency compared to existing systems. -
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.
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How We Help You with Wireless Networks Projects
At UniPhD, we provide complete guidance and support for Wireless Networks ieee projects for MTech, ME, Master’s, and PhD students. Our team assists you at every stage from topic selection to coding, report writing, and result analysis.
We also help you choose a suitable IEEE base paper and guide you in developing your project using Python-based tools and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, Flask, and Streamlit. In addition, we support implementation and simulation through platforms like MATLAB, Simulink, and NS2, depending on project requirements.
Our experts guide students all over India, including in Mumbai, Delhi, Bangalore, Hyderabad, Ahmedabad, Chennai, Kolkata, Pune, Jaipur, and Surat. We also assist students in the USA, UK, Canada, Australia, Singapore, Malaysia, and Thailand. They have extensive experience in computer science, electronics, electrical and all engineering domains.
Wireless Networks Thesis and Dissertation Writing
UniPhD has a team of experienced academic writers who specialize in Wireless Networks research and thesis development. We offer fast-track dissertation writing services to help you complete your Wireless Networks thesis or dissertation smoothly and on time.
Our M.E., M.Tech, Masters, MS abroad, and PhD theses are developed according to individual university guidelines and checked with plagiarism detection tools to ensure originality and quality.
Wireless Networks Research Paper Publishing Support
UniPhD provides complete support for research paper writing, editing, and proofreading to help you publish your work in reputed journals or conferences. We accept documents in Microsoft Word, RTF, or LaTeX formats and ensure your paper meets publication standards.
Project Synopsis and Presentation Support
We help you prepare your project synopsis, including the problem definition, objectives, and motivation for your dissertation. Our team also provides complete PPT, documentation, and tutorials to make your final presentation successful. You can also download complete project resources, including source code, a project report, a PPT, a tutorial, documentation, and a research paper for your Wireless Networks final year project.
Wireless Networks Research Support for PhD Scholars
UniPhD offers advanced Wireless Networks research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.
Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.