  In this paper, we aim to improve the Quality-of-Service (QoS) of
Ultra-Reliability and Low-Latency Communications (URLLC) in
interference-limited wireless networks. To obtain time diversity within the
channel coherence time, we first put forward a random repetition scheme that
randomizes the interference power. Then, we optimize the number of reserved
slots and the number of repetitions for each packet to minimize the QoS
violation probability, defined as the percentage of users that cannot achieve
URLLC. We build a cascaded Random Edge Graph Neural Network (REGNN) to
represent the repetition scheme and develop a model-free unsupervised learning
method to train it. We analyze the QoS violation probability using stochastic
geometry in a symmetric scenario and apply a model-based Exhaustive Search (ES)
method to find the optimal solution. Simulation results show that in the
symmetric scenario, the QoS violation probabilities achieved by the model-free
learning method and the model-based ES method are nearly the same. In more
general scenarios, the cascaded REGNN generalizes very well in wireless
networks with different scales, network topologies, cell densities, and
frequency reuse factors. It outperforms the model-based ES method in the
presence of the model mismatch.
