Resource Allocation Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Resource Allocation projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Resource Allocation Final Year Projects

Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Industrial IoT in MEC Federation System

This study focuses on improving smart industrial systems that use IoT devices. These devices have limited power and memory, so the project uses mobile edge computing to help them process data more efficiently. The researchers developed a smart method using deep learning to decide how tasks are shared and resources are used across devices. Their approach reduces energy use and processing delays in real-world scenarios.
High-Efficiency Resource Allocation Scheme Introducing the Concept of Resource Sharing Paths in Industrial IoT

This project focuses on improving industrial wireless networks used in smart factories. It addresses the problem of resource wastage when many devices send data along overlapping routes. The researchers propose a method that shares communication resources on common paths and considers network conditions like congestion. Their simulations show this approach uses resources more efficiently and handles more devices than traditional methods.
A DRL-Based Automated Algorithm Selection Framework for Cross-Layer QoS-Aware Scheduling and Antenna Allocation in Massive MIMO Systems

This project improves how mobile networks manage many antennas and users at the same time. It uses a learning model that picks the best scheduling and antenna methods based on current network traffic. The system learns from experience and adjusts its choices to keep users satisfied. Tests show it serves more users compared to fixed or traditional methods.
Congestion Control in Autonomous Resource Selection of Cellular-V2X

This project focuses on improving communication between vehicles in intelligent transportation systems. It addresses network congestion and communication problems when cellular coverage is absent. The researchers propose a method that controls vehicle transmission power and channel usage to reduce traffic on the network. Their solution improves performance and makes vehicle communication more reliable and flexible.
Cost-Aware DU Placement and Flow Routing in 5G Packet xHaul Networks

This project focuses on improving 5G network efficiency by deciding where to place processing units and how to route data flows between them. It aims to reduce overall costs while meeting strict delay requirements for data. The researchers use advanced mathematical methods and smart heuristics to find the best solutions, especially for large networks where exact methods are too slow. Their experiments show the approach works well and accurately predicts network delays.
Machine Learning Empowered Emerging Wireless Networks in 6G Recent Advancements Challenges and Future Trends

This project explains how future 6G networks will use machine learning to make communication faster, smarter, and more reliable. It shows how ML helps the network manage resources on its own and solve problems like energy use, delays, and smooth connections. The paper reviews different ML methods and how they improve new types of networks, such as device-to-device and vehicular systems. It also discusses open challenges and future research directions for building intelligent 6G networks.
Resource Allocation in Multi-Cluster Cognitive Radio Networks With Energy Harvesting for Hybrid Multi-Channel Access

This project studies a wireless network where secondary users can share the radio spectrum with primary users while also harvesting energy from their signals. The goal is to improve data rates and energy efficiency without causing interference to primary users. The system decides how long to sense the spectrum and how much power to use to maximize energy efficiency. Simulations show the new method performs better than existing approaches, balancing data throughput and energy use.

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Resource Allocation Project Synopsis & Presentation

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Resource Allocation Final Year Projects with Source Code