#network-optimization2 articles

2 articles

AIBullisharXiv – CS AI · 5h ago1

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Enhancing User Throughput in Multi-panel mmWave Radio Access Networks for Beam-based MU-MIMO Using a DRL Method

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.

AINeutralarXiv – CS AI · 5h ago1

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Network Topology Optimization via Deep Reinforcement Learning

Researchers propose DRL-GS, a deep reinforcement learning algorithm that optimizes network topology design by combining a verifier, graph neural network, and DRL agent. The approach addresses limitations of traditional heuristic methods by efficiently searching large topology spaces while incorporating management constraints.

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