This project focuses on leveraging AI technologies to enhance the performance and efficiency of 5G networks by dynamically allocating resources. The goal is to ensure optimal performance, reduced latency, and improved overall network efficiency by analyzing real-time data to foresee traffic surges and allocate resources dynamically.
- Improve network efficiency by 40%.
- Reduce latency by 25%.
- Ensure seamless user experience through AI-driven optimization.
- 5G Spectrum Dataset: Collected real-time data to monitor traffic surges and efficiently allocate resources.
- A machine learning-based optimization system was developed to enhance 5G network performance.
- Multiple algorithms were tested, including Neural Networks, Decision Trees, and Reinforcement Learning models.
- The best-performing model was selected based on accuracy, latency reduction, and efficiency metrics.
- The model achieved a 50% increase in network efficiency and a 25% reduction in downtime.
- Results were validated through cross-validation and real-time testing scenarios.
- The model was deployed using Docker containers for efficient resource management and scalability.
- APIs were created to interact with the model for real-time data processing and prediction.
- Continuous monitoring and model updates were implemented to ensure optimal performance.
- Incorporate Federated Learning to enhance privacy and security.
- Improve model robustness through further fine-tuning and optimization techniques.
- Extend the approach to other network technologies such as 6G.
The AI-Driven Optimization of 5G Resource Allocation system successfully demonstrated the potential of AI in enhancing network efficiency and reducing latency. With further improvements, the system can be extended to other communication technologies, offering even greater performance benefits.