Constrained Channel

Constrained channel research focuses on optimizing information transmission and processing when bandwidth or communication resources are limited. Current efforts center on developing efficient quantization and compression techniques, often leveraging adaptive algorithms and model-free learning approaches, to minimize information loss while maintaining performance in tasks like control systems, bandit problems, and federated learning. This work is crucial for advancing applications requiring real-time decision-making or distributed computation under resource constraints, such as autonomous navigation, remote sensing, and large-scale machine learning. The ultimate goal is to achieve near-optimal performance even with severely limited communication capabilities.