# Remote Interference Discrimination Testbed Employing AI Ensemble Algorithms for 6G TDD Networks

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## Abstract

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## 1. Introduction

- A remote interference discrimination testbed is originally proposed, which adopts 5,520,000 TDD network-side interfered data to discriminate the remote interference. A large number of measurement data could effectively appraise the interference discrimination ability of different AI algorithms;
- The testbed verifies the interference discrimination ability of two types of a total of nine AI algorithms, which lays the foundation for the application of the testbed in different hardware environments;
- According to the consistent comparison, numerical results illustrate that the ensemble algorithm achieves an average accuracy of 12% higher than the single model algorithm. The work fills the gap of remote interference in the 6G communication scenario and helps mobile operators improve network optimization capabilities under remote interference.

## 2. Related Work and Testbed Design

#### 2.1. Related Work

#### 2.2. Testbed Design

#### 2.2.1. Meteorological Factors

#### 2.2.2. Network Factors

## 3. AI-Based Discriminant Algorithms

#### 3.1. Single Model Algorithms

#### 3.1.1. kNN

#### 3.1.2. SVM

#### 3.1.3. NB

#### 3.2. Ensemble Algorithms

#### 3.2.1. RF

#### 3.2.2. Bagging

#### 3.2.3. Boosting

#### 3.2.4. Stacking

## 4. Interference Discrimination Experiments

#### 4.1. Interference Dataset

#### 4.2. Algorithm Settings

#### 4.3. Sensitivity of the Algorithms to the Data Size

#### 4.4. Sensitivity of the Algorithms to IR

#### 4.5. Robustness Analysis of the Algorithms

#### 4.6. Time Complexity

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

IoT | Internet of Things |

TDD | Time-Division Duplex |

GP | Guard Period |

kNN | k-Nearest Neighbors |

SVM | Support Vector Machine |

NB | Naive Bayes |

RF | Random Forest |

AdaBoost | Adaptive Boosting |

GBDT | Gradient Boosting Decision Tree |

XGBoost | Xtreme Gradient Boosting |

Bagging | Bootstrap Aggregating |

Stacking | Stacked Generalization |

PE | Parabolic Equation |

IR | Imbalance Ratio |

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**Figure 1.**Remote interference in TDD system, in which GP is the guard period and PTS is the pilot time slot.

**Figure 8.**Accuracy and recall results of single model algorithms and ensemble algorithms with different data sizes. (

**a**) Accuracy results. (

**b**) Recall results.

**Figure 9.**Accuracy and recall results of single model algorithms and ensemble algorithms with different IR. (

**a**) Accuracy results. (

**b**) Recall results.

Designation | Configuration |
---|---|

Core | i5-4210 H 2.90 GHz |

Operating system | Windows 10 |

Random-access memory | 12.0 GB |

Python | 3.7 |

Tensorflow | 2.0.0 |

Category | Algorithms | Parameters | Value | |
---|---|---|---|---|

Single model algorithms | kNN | Number of neighbors | 1 | |

SVM | Kernel | Linear/Radial basis function | ||

Maximum number of iterations | 100 | |||

NB | Type | Gaussian/Bernoulli/Complement | ||

Ensemble algorithms | RF | Number of trees in the forest | 100 | |

Bagging | Number of base estimators in the ensemble | 100 | ||

Boosting | AdaBoost | Maximum number of estimators | 500 | |

Learning rate | 0.01 | |||

GBDT | Number of boosting stages to perform | 100 | ||

Learning rate | 0.01 | |||

XGBoost | Number of decision trees | 100 | ||

Learning rate | 0.1 | |||

Stacking | Estimators | Lr/rf/kNN/cart/svc/bayes | ||

Final estimator | LogisticRegression |

IR | 5:1 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

Size of Training Data | 20,000 | 30,000 | 40,000 | 50,000 | 60,000 | |||||||

Indicators | Acc | Recall | Acc | Recall | Acc | Recall | Acc | Recall | Acc | Recall | ||

Single model algorithms | kNN | 61.93 | 36.46 | 63.65 | 38.14 | 64.17 | 41.21 | 65.83 | 43.49 | 65.52 | 42.67 | |

SVM | 50.00 | 0.00 | 50.02 | 0.46 | 50.01 | 0.33 | 50.00 | 0.00 | 50.00 | 0.00 | ||

NB | 54.74 | 17.58 | 54.76 | 17.16 | 54.69 | 18.13 | 55.14 | 18.19 | 54.74 | 17.73 | ||

Ensemble algorithms | RF | 70.18 | 43.23 | 71.27 | 45.61 | 72.38 | 48.07 | 73.83 | 50.73 | 74.00 | 50.92 | |

Bagging | 76.03 | 59.04 | 78.32 | 62.15 | 79.67 | 64.84 | 79.67 | 65.61 | 81.44 | 67.93 | ||

Boosting | AdaBoost | 53.75 | 7.85 | 54.38 | 8.89 | 54.65 | 9.88 | 54.50 | 9.33 | 54.12 | 9.23 | |

GBDT | 50.00 | 0.00 | 50.00 | 0.00 | 50.00 | 0.00 | 50.00 | 0.00 | 50.00 | 0.00 | ||

XGBoost | 69.58 | 41.89 | 70.56 | 43.31 | 71.98 | 46.73 | 72.49 | 47.98 | 72.88 | 48.14 | ||

Stacking | 71.49 | 45.99 | 72.82 | 47.95 | 74.68 | 52.34 | 76.96 | 56.78 | 77.28 | 57.38 |

Size of Training Data | 40,000 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

IR | 3:1 | 5:1 | 7:1 | 9:1 | 11:1 | |||||||

Indicators | Acc | Recall | Acc | Recall | Acc | Recall | Acc | Recall | Acc | Recall | ||

Single model algorithms | kNN | 67.10 | 51.57 | 64.17 | 41.21 | 61.92 | 34.26 | 61.59 | 30.98 | 60.26 | 27.55 | |

SVM | 50.99 | 2.33 | 50.01 | 0.33 | 50.00 | 0.00 | 50.00 | 0.00 | 50.00 | 0.00 | ||

NB | 54.71 | 21.04 | 54.69 | 18.13 | 54.52 | 15.90 | 54.30 | 14.96 | 54.09 | 13.81 | ||

Ensemble algorithms | RF | 77.51 | 60.24 | 72.38 | 48.07 | 69.35 | 40.99 | 65.91 | 33.29 | 63.61 | 28.59 | |

Bagging | 82.37 | 72.89 | 79.67 | 64.84 | 76.37 | 57.16 | 73.68 | 51.20 | 71.37 | 46.33 | ||

Boosting | AdaBoost | 54.68 | 9.71 | 54.65 | 9.88 | 52.39 | 5.06 | 51.98 | 4.04 | 51.90 | 3.82 | |

GBDT | 50.00 | 0.00 | 50.00 | 0.00 | 50.00 | 0.00 | 50.00 | 0.00 | 50.00 | 0.00 | ||

XGBoost | 77.85 | 60.69 | 71.98 | 46.73 | 67.49 | 36.37 | 64.04 | 29.11 | 61.19 | 23.08 | ||

Stacking | 81.04 | 67.70 | 74.68 | 52.34 | 70.45 | 42.63 | 66.26 | 33.79 | 64.29 | 29.81 |

Indicator | Accuracy | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

Size of Training Data | 20,000 | 30,000 | 40,000 | 50,000 | 60,000 | |||||||

Proportion of Abnormal Data | 1% | 5% | 1% | 5% | 1% | 5% | 1% | 5% | 1% | 5% | ||

Single model algorithms | kNN | 77.76 | 91.50 | 80.33 | 93.87 | 84.09 | 97.23 | 86.85 | 93.97 | 86.95 | 96.83 | |

SVM | 63.63 | 63.63 | 63.63 | 63.63 | 63.63 | 63.63 | 63.63 | 63.63 | 63.63 | 63.63 | ||

NB | 63.63 | 63.83 | 63.43 | 63.73 | 63.54 | 63.93 | 63.54 | 63.43 | 63.43 | 63.63 | ||

Ensemble algorithms | RF | 88.73 | 95.25 | 90.31 | 95.55 | 90.61 | 96.64 | 91.40 | 95.25 | 92.29 | 95.75 | |

Bagging | 89.32 | 96.34 | 90.51 | 96.24 | 91.10 | 96.73 | 92.19 | 95.06 | 92.68 | 96.65 | ||

Boosting | AdaBoost | 63.63 | 83.59 | 63.63 | 83.79 | 63.63 | 83.70 | 63.63 | 80.83 | 63.63 | 80.73 | |

GBDT | 63.63 | 63.63 | 63.63 | 63.63 | 63.63 | 63.63 | 63.63 | 63.63 | 63.63 | 63.63 | ||

XGBoost | 89.42 | 96.34 | 90.90 | 96.54 | 91.69 | 97.03 | 92.49 | 95.84 | 92.88 | 97.13 | ||

Stacking | 63.63 | 96.44 | 63.63 | 96.73 | 63.63 | 97.92 | 63.63 | 96.54 | 63.63 | 97.82 |

Algorithms | Time Complexity | Order | Test |
---|---|---|---|

kNN | $O\left(kn\right)$ | $O\left(n\right)$ | 1.49 s |

SVM | $O\left({n}^{2}\right)$ | $O\left({n}^{2}\right)$ | 8.20 s |

NB | $O\left(ckn\right)$ | $O\left(n\right)$ | 1.02 s |

RF | $O\left(kdmnlogn\right)$ | $O\left(nlogn\right)$ | 3.98 s |

Bagging | $O\left(Base\right)$ | $O\left(Base\right)$ | 11.52 s |

AdaBoost | $O\left(knlogn\right)$ | $O\left(nlogn\right)$ | 3.49 s |

GBDT | $O\left(kdnlogn\right)$ | $O\left(nlogn\right)$ | 2.46 s |

XGBoost | $O\left(md\right|\left|x\right|{|}_{0}+\left|\right|x\left|{|}_{0}logn\right)$ | $O\left(logn\right)$ | 1.79 s |

Stacking | $O\left(Base\right)$ | $O\left(Base\right)$ | 47.78 s |

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**MDPI and ACS Style**

Zhang, H.; Zhou, T.; Xu, T.; Hu, H.
Remote Interference Discrimination Testbed Employing AI Ensemble Algorithms for 6G TDD Networks. *Sensors* **2023**, *23*, 2264.
https://doi.org/10.3390/s23042264

**AMA Style**

Zhang H, Zhou T, Xu T, Hu H.
Remote Interference Discrimination Testbed Employing AI Ensemble Algorithms for 6G TDD Networks. *Sensors*. 2023; 23(4):2264.
https://doi.org/10.3390/s23042264

**Chicago/Turabian Style**

Zhang, Hanzhong, Ting Zhou, Tianheng Xu, and Honglin Hu.
2023. "Remote Interference Discrimination Testbed Employing AI Ensemble Algorithms for 6G TDD Networks" *Sensors* 23, no. 4: 2264.
https://doi.org/10.3390/s23042264