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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|March 4, 2026 at 05:00 AM|1 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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