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Autonomous handover parameter optimisation for 5G cellular networks using deep deterministic policy gradient
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DOI：10.1016/j.eswa.2023.122871.png)
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Abstract
En 中文
The ultra-dense network (UDN) is considered a vital technology for 5G mobile communications due to its ability to transmit high data rates in high-traffic environments. However, it also creates new challenges, such as increased interference and difficulty managing mobility. To ensure seamless base station connectivity and maintain a high quality of service, a reliable handover algorithm is necessary, especially in a UDN where the cell size is small. This paper proposes an optimisation method for handover parameters based on the Deep Deterministic Policy Gradient (DDPG) algorithm. It adjusts the handover margin (HOM) to determine the handover trigger points accurately and dynamically. Simulation results indicate that the system's mobility performance has been greatly improved while maintaining high throughput and low latency at different speeds.
Keywords:
DDPG
Handover parameters
Reinforcement learning
Self-optimisation
5G UDN
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