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Conference Paper
Reinforcement Learning-Based Beam Selection in mmWave Networks Using Madrid Grid
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- Authors
- Nam-I Kim, Jee-Hyeon Na
- Issue Date
- 2024-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2024, pp.1123-1124
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we propose a beam selection method using reinforcement learning for mmWave networks. The proposed method is evaluated within a realistic simulation environment based on Madrid Grid model. By leveraging reinforcement learning, base stations can autonomously select optimal beams to maximize data rates, overcoming challenges associated with beamforming in mmWave frequencies. Simulation results demonstrate that the AI-based approach effectively improves network performance, increasing both coverage and capacity in dynamic environments.
- KSP Keywords
- Based Approach, Beam selection, Dynamic Environment, Grid model, Learning-based, MmWave networks, Network performance, Realistic simulation, Reinforcement learning(RL), Selection method, Simulation Environment