# Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring

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## Abstract

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## 1. Introduction

- Our proposed schemes directly process the maps with terrain information by Convolutional Neural Network (CNN) to obtain large-scale propagation/diffraction loss or shadow fading parameters. A framework including data set generation, network structure, training, and metrics evaluation has been constructed to research into the combination of CV and terrain-related radio propagation.
- A direct link is established by a CNN between topographic map to propagation/diffraction loss for a pair of transmitter and receiver on the map. Furthermore, the pathloss between a transmitter and multiple receivers can be predicted in one batch by taking advantage of the multiple parallel outputs defined for the CNN, which greatly enhances the computation efficiency and lays the foundation for extending the scheme to predict the pathloss from a transmitter to a coverage area in a very fast way.
- The quantitative relation is found between the terrain fluctuation pattern to correlation distance of shadow fading through a CNN model that can process a map of a coverage area, and the results would help configure radio access networks, e.g., to optimize handover performance.

## 2. Diffraction Loss Prediction Based on CNN

#### 2.1. Prediction Method

#### 2.2. Data Set Generation

_{L}diffraction losses recorded.

_{L}= 100 loss values simultaneously.

_{1}, d

_{2}are illustrated as in Figure 4. h represents the height above the connection of transmitter and receiver and d

_{1}and d

_{2}are the distances between transmitter and the knife edge and receiver and the knife edge, respectively. A single parameter v combining all the geometric parameters is first derived by Equation (5); then Equations (3) and (4) give the sine term and cosine term of Fresnel integral respectively; the linear value of diffraction loss is calculated by Equation (2) with the two terms of Fresnel integral; finally the dB value of diffraction loss is derived by Equation (1).

#### 2.3. CNN Structure and Performance Metrics

_{L}= 100. The loss function is defined as mean square error (MSE) between the predicted loss value and the corresponding label value, both in dB, expressed by

_{L}locations, ${l}_{i}$ is the label’s value, and ${\widehat{l}}_{i}$ is the predicted loss value.

## 3. Correlation Distance Prediction Based on CNN for Shadow Fading

#### 3.1. Prediction Method

#### 3.2. Data Set Generation

_{k}; ${S}_{W}\left(u,v\right)$ is the 2D Fourier transformation of a 2D correlation function, represented as below:

#### 3.3. CNN Structure and Performance Metrics

## 4. Simulation Results

#### 4.1. Results of Diffraction Loss Prediction

#### 4.2. Results of Shadow Fading Correlation Distance Extraction

## 5. Future Work

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Loss prediction method based on altitude topographic map. (

**a**) Model-based method; (

**b**) CNN-based method.

**Figure 9.**Error distribution of diffraction loss prediction. (

**a**) Absolute error; (

**b**) Normalized error.

**Figure 11.**Error distribution of correlation distance extraction. (

**a**) Absolute error; (

**b**) Normalized error.

Hyperparameters | Net A, B and C | Net D, E and F |
---|---|---|

batch size | 1 | 16 |

initial learning rate | 0.00001 | 0.0001 |

epoch | 200 | 100 |

learning rate decay | exponential decay | |

optimizer | Adam | |

activation | ReLu |

Net A | Net B | Net C | ||
---|---|---|---|---|

absolute error (dB) | 50% | 0.095 | 0.061 | 0.050 |

90% | 0.816 | 0.418 | 0.300 | |

95% | 1.370 | 1.074 | 0.953 | |

normalized error (%) | 50% | 1.006 | 0.632 | 0.518 |

90% | 7.120 | 4.655 | 3.577 | |

95% | 12.434 | 9.634 | 8.238 | |

processing time (ms) | per image | 6.28 | 6.41 | 6.65 |

per point | 0.0628 | 0.0641 | 0.0665 |

Net D | Net E | Net F | ||
---|---|---|---|---|

absolute error (dB) | 50% | 0.210 | 0.207 | 0.159 |

90% | 0.503 | 0.565 | 0.591 | |

95% | 0.673 | 0.708 | 0.752 | |

normalized error (%) | 50% | 2.181 | 2.377 | 1.747 |

90% | 5.103 | 5.521 | 5.267 | |

95% | 6.472 | 6.716 | 6.423 |

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**MDPI and ACS Style**

He, J.; Xing, Z.; Xiang, T.; Zhang, X.; Zhou, Y.; Xi, C.; Lu, H.
Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring. *Sensors* **2021**, *21*, 5688.
https://doi.org/10.3390/s21175688

**AMA Style**

He J, Xing Z, Xiang T, Zhang X, Zhou Y, Xi C, Lu H.
Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring. *Sensors*. 2021; 21(17):5688.
https://doi.org/10.3390/s21175688

**Chicago/Turabian Style**

He, Jialuan, Zirui Xing, Tianqi Xiang, Xin Zhang, Yinghai Zhou, Chuanyu Xi, and Hai Lu.
2021. "Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring" *Sensors* 21, no. 17: 5688.
https://doi.org/10.3390/s21175688