Advancements in Ultra-Reliable Low-Latency Communications

The field of ultra-reliable low-latency communications (URLLC) is witnessing significant developments, driven by the need for efficient and reliable communication in emerging applications such as autonomous driving, robotics, and industrial automation. Research is focused on enhancing the performance of URLLC systems, with a particular emphasis on improving interference coordination, scheduling, and resource allocation. Novel approaches, including the integration of machine learning and graph convolutional networks, are being explored to optimize link priorities and adapt to dynamic network conditions. These advancements have the potential to significantly improve the reliability and latency of URLLC systems, enabling their widespread adoption in critical applications. Noteworthy papers in this area include:

  • A paper proposing a GCN-Driven Reinforcement Learning approach for probabilistic real-time guarantees in industrial URLLC, which demonstrates significant improvements in SINR over existing methods.
  • A paper presenting an Integrated Sensing and Communication System for time-sensitive targets with random arrivals, which achieves higher eMBB transmission rates while satisfying URLLC and sensing constraints.

Sources

Adaptive Composition of Machine Learning as a Service (MLaaS) for IoT Environments

Black-Box Edge AI Model Selection with Conformal Latency and Accuracy Guarantees

Adaptive determinantal scheduling with fairness in wireless networks

Distributed Learning for Reliable and Timely Communication in 6G Industrial Subnetworks

CNN-Enabled Scheduling for Probabilistic Real-Time Guarantees in Industrial URLLC

GCN-Driven Reinforcement Learning for Probabilistic Real-Time Guarantees in Industrial URLLC

An Integrated Sensing and Communication System for Time-Sensitive Targets with Random Arrivals

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