# Supervised Learning Technique for First Order Multipaths Identification of V2V Scenario

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## Abstract

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## 1. Introduction

- We presented a statistical analysis of the characteristics of the propagation paths and investigated how these characteristics impact the FOMPs identification.
- We proposed an efficient solution based on supervised classifiers to distinguish between the FOMP and the HOMP in blocked V2V communication by applying six supervised classifiers. The training dataset was generated by using a ray tracing technique.
- We tested the proposed classifiers using different strategies. Then their performance is compared in terms of several well-known metrics such as accuracy and precision. Furthermore, since this work is interested in the FOMPs, we presented a particular metric based on the estimation error of the HOMP as FOMP.

## 2. Related Works

## 3. Background on ML Classifiers

#### 3.1. Supervised Classisfication Algorithms

#### 3.2. Features Selection

#### 3.2.1. Received Power (RP)

#### 3.2.2. Propagation Time

#### 3.2.3. Angular Variation

#### 3.3. Assessment Criteria

#### 3.3.1. Accuracy

#### 3.3.2. Precision

#### 3.3.3. Recall

#### 3.3.4. Mean Absolute Error (MAE)

#### 3.3.5. Root Mean Squared Error (RMSE)

#### 3.3.6. HOMP Prediction Error (HOMPPE)

## 4. Methodology

#### 4.1. Simulation Setup and SBR Validation

#### 4.2. V2V Configuration and Data Collection

#### 4.3. Data Preparation and Supervised Classification Models

## 5. Result and Discussion

#### 5.1. Ray Tracing Validation

#### 5.2. Statistical Analysis of the Propagation Characteristics

#### 5.3. Discussion of Classification Results

#### 5.3.1. Evaluation of the Classifiers Prediction

#### 5.3.2. Impact of the Features Selection

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**Wireless InSite ray tracer (

**a**) 3D Geometric Structure of KLCC and (

**b**) UMi visualization with LOS area (blue color) and NLOS area (red color).

**Figure 6.**Distribution of the factors in the FOMPs and HOMPs (

**a**) Received power, (

**b**) TOA, (

**c**) AAOA, and (

**d**) EAOA.

**Figure 7.**PDF of different extracted characteristics: (

**a**) Received power, (

**b**) TOA, (

**c**) AAOA, and (

**d**) EAOA.

EM of Material Properties | |||
---|---|---|---|

Electro Magnetic Parameters | Material | ɛ_{r} | σ [S/m] |

Buildings (Concrete) | 5.31 | 0.8967 | |

Vegetation-Leaf | 26 | 0.39 | |

Vegetation-Branch | 20 | 0.39 | |

Vehicle (Metal) | 1.00 | 10 × 10^{7} | |

UMi specifications | |||

Antenna | Type | Omnidirectional | |

Polarization | Vertical | ||

Gain | 27 dBi | ||

Height | Transmitter = 10 m Receiver = 2 m | ||

Transmitted Power | 42.0 dBm | ||

UMi size | 200 m × 400 m | ||

Frequency | 28 GHz | ||

Ray tracing Technique | SBR | ||

Model | Full 3D |

Antenna | Type | Isotropic |

Polarization | Vertical | |

Gain | 10 dBi | |

Height | Transmitter = 2 m Receiver = 2 m | |

Transmitted Power | 14.6 dBm | |

Frequency | 28 GHz | |

Bandwidth | 450 MHz | |

Number of reflections | 6 | |

Number of Paths | 25 | |

Ray Spacing | 0.15 | |

Ray tracing Technique | SBR | |

Model | Full 3D |

Scenario | Parameters | Outdoor Urban Environment | |
---|---|---|---|

Previous Work [36,37,38,39] | Simulation | ||

LOS | n | 2–4 | 2.148 |

NLOS | n | 2.7–6 | 3.095 |

Classifier | Optimized Hyperparameters |
---|---|

DT | Max. No. of split: 50 |

NB | - |

SVM | Kernel function: Gaussian. |

KNN | K: 7, Distance metric: Euclidean. |

RF | Max. No. of split: 30, Number of destination trees: 15. |

ANN | No. of layers: 3, Layers size: 40, Activation function: ReLU, Max. No. of epochs: 500. |

Classifier | Accuracy % | Precision % | HOMPPE % | MAE | RMSE |
---|---|---|---|---|---|

DT | 94.5 | 92.9 | 5.8 | 0.061 | 0.247 |

NB | 87.8 | 91.8 | 16.7 | 0.135 | 0.368 |

SVM | 92.4 | 85.7 | 2.7 | 0.041 | 0.203 |

KNN | 94.1 | 96.4 | 4.2 | 0.047 | 0.217 |

RF | 95.9 | 92.9 | 3.3 | 0.041 | 0.203 |

ANN | 96.5 | 92.9 | 2.3 | 0.034 | 0.184 |

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## Share and Cite

**MDPI and ACS Style**

Bakhuraisa, Y.A.; Abd Aziz, A.B.; Geok, T.K.; Abu Bakar, N.B.; Jamian, S.B.; Mustakim, F.B.
Supervised Learning Technique for First Order Multipaths Identification of V2V Scenario. *World Electr. Veh. J.* **2023**, *14*, 109.
https://doi.org/10.3390/wevj14040109

**AMA Style**

Bakhuraisa YA, Abd Aziz AB, Geok TK, Abu Bakar NB, Jamian SB, Mustakim FB.
Supervised Learning Technique for First Order Multipaths Identification of V2V Scenario. *World Electric Vehicle Journal*. 2023; 14(4):109.
https://doi.org/10.3390/wevj14040109

**Chicago/Turabian Style**

Bakhuraisa, Yaser A., Azlan B. Abd Aziz, Tan K. Geok, Norazhar B. Abu Bakar, Saifulnizan B. Jamian, and Fajaruddin B. Mustakim.
2023. "Supervised Learning Technique for First Order Multipaths Identification of V2V Scenario" *World Electric Vehicle Journal* 14, no. 4: 109.
https://doi.org/10.3390/wevj14040109