Worst Case User Throughput

Worst-case user throughput focuses on optimizing network performance to guarantee a minimum level of service for all users, even under challenging conditions like high congestion or interference. Current research emphasizes developing algorithms and models, including graph neural networks, reinforcement learning, and various neural network architectures (e.g., ConvLSTMs, autoencoders), to improve throughput in diverse applications such as wireless communication networks, large language model serving, and robotic swarms. This research is crucial for ensuring reliable and efficient operation of various systems, particularly in scenarios with stringent performance requirements or unpredictable conditions.