# ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs

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## Abstract

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## 1. Introduction

- We propose a GNN model that predicts the achieved throughput in highly dense IEEE 802.11 WLANs using CB. To the best of our knowledge, this is the first time GNNs are applied to this problem.
- We compare our approach with recent state-of-the-art DL and ML approaches and discuss how different features impact the prediction accuracy. Based on the available features, a given model can be optimally selected.
- Our proposal accurately predicts the throughput generating a model that can be employed in future intelligent decision frameworks for CB, similar to [9].

## 2. Related Work

- Instead of defining an analytical model that does not scale when the number of nodes in the environment increases, we propose several ML approaches that learn from data. The proposed approaches take care of the complex task of defining wireless interactions.
- Unlike [15], our approach does not define the best CB policy that a WLAN needs to apply to maximize its reward. Our goal is that the ML model learns how the wireless interactions affect the throughput of a given WLAN without any explicit assumptions, such as user demand or user mobility patterns. This approach can later be used as a function approximator in such RL approaches.

## 3. Motivation for Learning Models in Next-Generation WLANs

## 4. A GNN Model for Performance Prediction in Next-Generation WLANs

- CB is a problem with a combinatorial action space in dense deployments, where the complexity increases exponentially with the size of the deployment and the number of possible channel configurations.
- The relationships between STAs and APs (connectivity, interference, among others) can be captured via a graph representation, i.e., there is one-to-one mapping between the network topology and the graph representation.
- GNNs are also proposed to solve multiple network optimization processes [23], given their ability to generalize to large-scale problems.
- GNNs can easily operate and generalize over environments with a changing topology and a variable number of nodes.

## 5. State-of-the-Art ML Models

## 6. Data Set

#### 6.1. Simulated Data Sets for Training Ml-Based Networking Solutions

#### 6.2. Data Set Generation

#### 6.3. Data Set Analysis

## 7. Results

#### 7.1. Training and Validation

#### 7.2. Performance Evaluation of the Proposed Models

## 8. Discussion

## 9. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**CB problem’s complexity is exacerbated by its combinatorial nature and the applied transmission policy.

**Figure 2.**Example of a WLAN deployment. On the left, the topological view of the deployment, some measurements are included. On the right, the graph representation of the deployment with a node feature

**N**

_{j}, an edge feature

**L**

_{i}and its adjacency matrix

**A**.

**Figure 3.**Summary of our GNN model. A high level overview is presented on the left, while the model architecture is presented on the right.

**Figure 5.**Spatial distribution per training and testing scenarios. Different colors represent the coverage area of a BSS. APs are located in the center of the circle.

Data Set | Scenarios | Map Size | № Deployments | Total Devices | APs Per Deployment | STAs Per Deployment | Mean–Std–Min–Max STA Throughput | Mean–Std–Min–Max AP Throughput |
---|---|---|---|---|---|---|---|---|

Training and Validation | 1a | 80 × 60 m | 100 | 78,078: 6000 APs 72,078 STAs | 12 | [10–20] | [6.93–6.99–0–88] Mbps | [83.29–52.24–0–400] Mbps |

1b | 70 × 50 m | 100 | 12 | [10–20] | ||||

1c | 60 × 40 m | 100 | 12 | [10–20] | ||||

2a | 60 × 40 m | 100 | 8 | [5–10] | ||||

2b | 50 × 30 m | 100 | 8 | [5–10] | ||||

2c | 40 × 20 m | 100 | 8 | [5–10] | ||||

Testing | 1 | 80 × 60 m | 50 | 9831: 1400 APs 8431 STAs | 4 | Random | N/A | N/A |

2 | 80 × 60 m | 50 | 6 | Random | ||||

3 | 80 × 60 m | 50 | 8 | Random | ||||

4 | 80 × 60 m | 50 | 10 | Random |

Parameter | Value | ||
---|---|---|---|

Training | Test | ||

Depl. | # APs | {8, 12} | {4, 6, 8, 10} |

APs location | Fixed to the center of the cell | ||

# STAs | {5–10, 10–20} | 5–10 | |

STAs location | Uniform at random | ||

Traffic profile | Downlink UDP | ||

Traffic load | Full buffer mode | ||

Channel allocation | Uniform at random | ||

PHY | Central freq. | 5 GHz | |

Path-loss model | See [31] | ||

Bandwidth | {20, 40, 80, 160} MHz | ||

# spatial streams | 1 | ||

Allowed MCS indexes | 1–12 | ||

MAC | Contention window | 32 (fixed) | |

Data and ACK length | 12,000/32 bits | ||

RTS and CTC length | 160/112 bits | ||

Max. A-MPDU | 1 | ||

DCB policy | Dynamic (see [12]) |

Feature | Definition | E1 | E2 | E3 | E4 | E5 | E6 | E7 | E8 | E9 | E10 | E11 | E12 | E13 | E14 | E15 | E16 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Node Type | Wireless node type, AP = 0, STA = 1 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||

x(m) | x-coordinate of the wireless node | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||

y(m) | y-coordinate of the wireless node | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||

Channel Configuration | Combination of Primary, minimum and maximum channel | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||

SINR | Signal to Interference plus Noise Ratio | ✓ | ✓ | ✓ | ✓ | ||||||||||||

Airtime | Percentage of time each AP occupies each of the assigned channels (mean) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||

Interference | Inter-AP interference sensed from every AP (mean) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||

RSSI | Received Signal Strength Indicator | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||

Distance | Euclidean distance between AP and STAs | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||

Bandwidth | Maximum channel bandwidth | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |

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**MDPI and ACS Style**

Soto, P.; Camelo, M.; Mets, K.; Wilhelmi, F.; Góez, D.; Fletscher, L.A.; Gaviria, N.; Hellinckx, P.; Botero, J.F.; Latré, S.
ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs. *Sensors* **2021**, *21*, 4321.
https://doi.org/10.3390/s21134321

**AMA Style**

Soto P, Camelo M, Mets K, Wilhelmi F, Góez D, Fletscher LA, Gaviria N, Hellinckx P, Botero JF, Latré S.
ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs. *Sensors*. 2021; 21(13):4321.
https://doi.org/10.3390/s21134321

**Chicago/Turabian Style**

Soto, Paola, Miguel Camelo, Kevin Mets, Francesc Wilhelmi, David Góez, Luis A. Fletscher, Natalia Gaviria, Peter Hellinckx, Juan F. Botero, and Steven Latré.
2021. "ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs" *Sensors* 21, no. 13: 4321.
https://doi.org/10.3390/s21134321