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Conference Paper
Multi-Agent Deep Reinforcement Learning-Based Handover Decision Method Incorporating a Dual Active Protocol Stack
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- Authors
- Nam-I Kim, Hyungsub Kim, Jee-Hyeon Na, Dongseok Roh
- Issue Date
- 2026-06
- Citation
- European Conference on Networks and Communications (EuCNC) 2026, pp.98-103
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we propose a multi-agent deep reinforcement learning-based dual active protocol stack handover decision method. With the recent arrival of the 5G era, heterogeneous network approaches have emerged. Consequently, decision-making to support the mobility of user equipment (UE), which must be handled by network cores and base stations, inevitably becomes highly complex. Moreover, the dual active protocol stack handover, newly released by 3GPP, requires more complex calculations to provide a better user experience for UE. To address the complexity of handover decision problems, we applied the recently popular deep reinforcement learning (DRL) method. DRL enables network operators to benefit from an artificial intelligence system that learns and autonomously decides, eliminating the need to devise complex handover policies or algorithms. We also adopted a simulation environment based on the Madrid grid model to consider a more realistic environment, referring to a highly realistic model based on actual cities. Results of learning and simulations performed in the Madrid grid environment are provided for validation of the proposed approach.
- Keyword
- deep reinforcement learning, dual active protocol stack, handover, heterogeneous network, Small-cell
- KSP Keywords
- Decision problems, Decision-making, Deep reinforcement learning, Grid Model, Handover decision, Learning-based, Protocol stack, Realistic environment, Realistic model, Simulation Environment, Small cells