Applying Deep Reinforcement Learning for Self-organizing Network Architecture
- Resource Type
- Conference
- Authors
- Tu, Yi-Hao; Ma, Yi-Wei; Li, Zhi-Xiang; Chen, Jiann-Liang; Tsukamoto, Kazuya
- Source
- 2023 IEEE 6th International Conference on Knowledge Innovation and Invention (ICKII) Knowledge Innovation and Invention (ICKII), 2023 IEEE 6th International Conference on. :16-20 Aug, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Wireless communication
Deep learning
Technological innovation
5G mobile communication
Key performance indicator
Reinforcement learning
Self-organizing networks
deep reinforcement learning
handover optimization
mobility load balancing
mobility robustness optimization
self-organizing networks
- Language
- ISSN
- 2770-4785
Inefficient resource allocation and unstable connection quality for mobile devices are the primary challenges of Self-Organizing Networks (SON). Frequent handovers between base stations result in a network burden imbalance. In contrast, unstable connection quality causes disconnection or signal interference between mobile devices and base stations, influencing network performance and reliability. In recent years, wireless communication technology has extensively used Reinforcement Learning (RL) to obtain the optimal strategy through continuous interaction between agents and their environments. Deep Reinforcement Learning (DRL) is based on Deep Neural Networks (DNN) to handle increasingly complex network situations. We proposed a SON architecture based on DRL in response to the aforementioned challenges. We described how the agent learns the optimal parameter settings through training based on various network scenarios to develop handover strategies and enhance overall network performance and resource utilization. The proposed framework can be applied to the present Fifth Generation (5G) network.