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Load Balancing in Mobile Networks Using Deep Reinforcement Learning and Traffic Prediction
Wireless communication networks are advancing at a rapid pace, driven by various challenges and ambitious goals. This rapid growth is driven by a range of applications, including technologies like the Internet of Things (IoT), as well as innovations in smart cities, autonomous vehicles, and more. Di...
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| Format: | Thesis |
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AUC Knowledge Fountain
2025
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| Summary: | Wireless communication networks are advancing at a rapid pace, driven by various challenges and ambitious goals. This rapid growth is driven by a range of applications, including technologies like the Internet of Things (IoT), as well as innovations in smart cities, autonomous vehicles, and more. Different applications demand specific performance criteria such as high data throughput, low latency, robust reliability, and efficient energy usage. In this thesis, we investigate two enhancements that can be adopted in wireless networks to tackle the challenges of resource optimization and network management. The motivation behind this is the fact that future networks will face challenges like severe congestion and varying traffic demands. The objective is to achieve higher network throughput and more data transmission by adjusting the network parameters. The first proposed approach introduces an enhanced self-optimization framework using deep reinforcement learning (RL) to dynamically adjust network parameters such as handover parameters, power levels, and MIMO technology. The proposed approach offers significant gains in network throughput by effectively balancing the load distribution. The proposed framework explores the trade-off between system complexity and performance improvement, demonstrating that adopting a scenario-aware optimized agent can outperform generalized agents under specific network conditions. The second approach we tackle is to adopt a proactive concept while controlling the network. The proposed approach is based on the ARIMA model used to predict the next states of the environment so that the RL agent considers them in the decision-making process. The simulation results demonstrate that the proposed approach leads to higher throughput and improved network performance, which underscores its potential as a robust alternative to the conventional agent existing in earlier works. |
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