Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?

• URL: http://arxiv.org/abs/2207.06918v1
• Date: Mon, 11 Jul 2022 05:49:41 GMT
• Title: Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?
• Authors: Yuhong Liu, Changyang She, Yi Zhong, Wibowo Hardjawana, Fu-Chun Zheng, and Branka Vucetic
• Abstract summary: We build a cascaded Random Edge Graph Neural Network (REGNN) to represent the repetition scheme and train it. We analyze the violation probability using geometry in a symmetric scenario and apply a model-based Exhaustive Search (ES) method to find the optimal solution.
• Score: 45.776476161876204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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