Meta-learning Approaches for Smart Antenna Systems in 5G Networks Using Reinforcement Learning and Artificial Intelligence | ||||
| Journal of Communication Sciences and Information Technology | ||||
| Volume 7, Issue 1, February 2025 PDF (447.65 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jcsit.2025.329210.1011 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Walid Dabour  
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| Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El Kom 32511, Egypt | ||||
| Abstract | ||||
| Smart antenna systems are critical for optimizing communication in 5G networks due to their ability to handle high data rates and dynamic environments. This paper presents a meta-learning framework that leverages machine learning (ML) and artificial intelligence (AI) to enhance the performance of smart antenna systems. We focus on reinforcement learning (RL) techniques for adaptive beamforming, interference management, and resource allocation. By incorporating meta-learning strategies, we enable the system to quickly adapt to new environments with minimal retraining, resulting in improved network efficiency and reliability. We demonstrate our approach through simulations and show significant performance gains over traditional methods. This paper demonstrates the potential of meta-learning in improving the adaptability of smart antenna systems in 5G networks. By leveraging reinforcement learning, our meta-learning framework significantly enhances the performance of beamforming, interference management, and resource allocation. The results show promising improvements in throughput and reliability, making this approach suitable for real-time 5G applications. Future work will explore the integration of multi-agent systems and collaborative meta-learning to further optimize network-wide performance. | ||||
| Keywords | ||||
| Smart Antenna Systems; Machine Learning; Beamforming; Interference Management; Resource Allocation | ||||
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