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In this paper, we study the problem of dynamic channel allocation for URLLCtraffic in a multi-user multi-channel wireless network where urgent packetshave to be successfully transmitted in a timely manner. We formulate theproblem as a finite-horizon Markov Decision Process with a stochasticconstraint related to the QoS requirement, defined as the packet loss rate foreach user. We propose a novel weighted formulation that takes into account boththe total expected reward (number of successfully transmitted packets) and therisk which we define as the QoS requirement violation. First, we use the valueiteration algorithm to find the optimal policy, which assumes a perfectknowledge of the controller of all the parameters, namely the channelstatistics. We then propose a Q-learning algorithm where the controller learnsthe optimal policy without having knowledge of neither the CSI nor the channelstatistics. We illustrate the performance of our algorithms with numericalstudies.

Risk-Sensitive Reinforcement Learning for URLLC Traffic in Wireless Networks