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Enhancing User Throughput in Multi-panel mmWave Radio Access Networks for Beam-based MU-MIMO Using a DRL Method
arXiv – CS AI|Ramin Hashemi, Vismika Ranasinghe, Teemu Veijalainen, Petteri Kela, Risto Wichman||2 views
🤖AI Summary
Researchers developed a deep reinforcement learning approach to optimize beam management in millimeter-wave radio access networks, achieving up to 16% throughput improvements and 3-7x latency reduction. The method uses adaptive beam selection based on real-time observations to enhance multi-user MIMO performance in practical network setups.
Key Takeaways
- →DRL-based beam management for mmWave networks demonstrates significant performance gains over legacy systems.
- →The approach achieves up to 16% increase in throughput and reduces latency by factors of 3-7x.
- →The framework incorporates spatial domain characteristics and real-time RSRP measurements for dynamic optimization.
- →Multi-panel antenna systems with MU-MIMO benefit from adaptive beam selection strategies.
- →The research addresses practical network deployment challenges in 5G/6G wireless infrastructure.
#deep-reinforcement-learning#mmwave#5g#wireless-networks#beam-management#mimo#network-optimization#telecommunications#research
Read Original →via arXiv – CS AI
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