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

arXiv: https://arxiv.org/abs/2207.06918 · HTML: https://mlsanity.com/paper/2207.06918/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 11 Jul 2022.

## Builds on (most cited first, 12 of 12)

- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks](https://mlsanity.com/paper/1909.01865/) (2019)
- [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020)
- [Learning Optimal Resource Allocations in Wireless Systems](https://mlsanity.com/paper/1807.08088/) (2018)
- [Joint Power and Blocklength Optimization for URLLC in a Factory Automation Scenario](https://mlsanity.com/paper/1911.13050/) (2019)
- [Resource Allocation and HARQ Optimization for URLLC Traffic in 5G Wireless Networks](https://mlsanity.com/paper/1804.09201/) (2018)
- [Analyzing Grant-Free Access for URLLC Service](https://mlsanity.com/paper/2002.07842/) (2020)
- [Cross-layer Design for Mission-Critical IoT in Mobile Edge Computing Systems](https://mlsanity.com/paper/1907.05210/) (2019)
- [Downlink MIMO HetNets: Modeling, Ordering Results and Performance Analysis](https://mlsanity.com/paper/1301.5034/) (2013)
- [Aggregation and Resource Scheduling in Machine-type Communication Networks: A Stochastic Geometry Approach](https://mlsanity.com/paper/1708.07691/) (2017)
- [A D2D-based Protocol for Ultra-Reliable Wireless Communications for Industrial Automation](https://mlsanity.com/paper/1710.01265/) (2017)
- [Spatio-temporal Modeling for Massive and Sporadic Access](https://mlsanity.com/paper/2001.11783/) (2020)

## Built on by (most cited first, 2 of 2)

- [Graph Neural Network-Based Bandwidth Allocation for Secure Wireless Communications](https://mlsanity.com/paper/2312.14958/) (2023)
- [Hybrid-Task Meta-Learning: A GNN Approach for Scalable and Transferable Bandwidth Allocation](https://mlsanity.com/paper/2401.10253/) (2023)

## Abstract (arXiv)

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.