Optimization Problem Projects for ME, MTech, Masters, MS abroad, and PhD students. These Optimization Problem 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 Optimization Problem Projects
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Attention Mechanism-Aided Deep Reinforcement Learning for Dynamic Edge Caching
This project focuses on improving mobile networks by predicting what content users will need and storing it on nearby devices before it is requested. It designs a smart system that decides what to cache and when, using a type of artificial intelligence. The goal is to reduce network traffic and make better use of limited storage and network resources. The proposed method was tested and shown to work effectively in improving speed and efficiency. -
Joint Optimization of Uplink Power and Computational Resources in Mobile Edge Computing-Enabled Cell-Free Massive MIMO
This project studies how to improve wireless networks and mobile computing together. It combines a type of advanced antenna system with edge computing to share both communication and computing resources efficiently. The goal is to save user device power, reduce delays, and improve data transmission and computation. The researchers propose and test new methods to manage these resources for better performance. -
Joint Resource Management and Pricing for Task Offloading in Serverless Edge Computing
This project studies how to manage computing resources and prices in edge servers to help devices run tasks faster and save energy. The researchers model the problem as a game between the server and the devices. They develop fast algorithms that decide which tasks to run on the server, how to price them, and what apps to store. Their methods increase server revenue while reducing energy use for devices. -
Minimizing the AoI in Resource-Constrained Multi-Source Relaying Systems Dynamic and Learning-Based Scheduling
This project studies a communication system where multiple sources send status updates to a destination through a relay. The goal is to deliver fresh information efficiently while using limited resources. The researchers designed smart scheduling methods using optimization and machine learning to decide which updates to send and when. Their methods were tested in simulations and showed up to 91% better performance than simple baseline approaches. -
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. -
Precoder Optimization Using Data Correlation for Wireless Data Aggregation
This project focuses on improving how data is collected from many sensors in a wireless network. It designs a method to send data more accurately while considering how the data from different sensors are related. The approach uses an optimization technique to reduce errors in the collected data. It performs better than older methods, especially when sensor data are closely related. -
Sensing-Assisted Eavesdropper Estimation An ISAC Breakthrough in Physical Layer Security
This project studies how communication systems can use sensing to improve security. The system detects potential eavesdroppers and directs signals to block them. It uses smart optimization to balance accurate detection and secure transmission. Overall, sensing helps the system stay safe while communicating efficiently. -
Sensor Response-Based Localization Using Spatial Correlation of Nongeotagged Data
This project focuses on finding the locations of sensor devices in future wireless networks without using GPS. It uses patterns in sensor responses to estimate where each sensor is placed. The method works even when sensors are close together and do not measure distance directly. Simulations show it can locate sensors with about 1-meter accuracy, which is useful for monitoring urban environments.
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