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Communication-Efficient Orchestrations for URLLC Service via Hierarchical Reinforcement Learning
However, with conventional RL methods, the decision variables (though being deployed at various network layers) are typically optimized in the same control loop, leading to significant practical limitations on the control loop's delay as well as excessive signaling and energy consumption.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Device Selection for the Coexistence of URLLC and Distributed Learning Services
Our simulation results confirm that our solution can significantly decrease the training delay of the distributed AI service while keeping the URLLC availability above its required threshold and close to the scenario where URLLC solely consumes all network resources.
