# Security–Reliability Analysis of AF Full-Duplex Relay Networks Using Self-Energy Recycling and Deep Neural Networks

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## Abstract

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## 1. Introduction

#### 1.1. Related Work and Motivation

#### 1.2. Contributions

- We propose a novel SWIPT-assisted AF-FD relay network to evaluate security and reliability trade-offs. In particular, in order to increase EH, the relay can harvest energy from the source and reuse the self-interference channel based on the PS protocol to attain battery-free operation;
- We derive the approximate OP for legitimate communications and the approximate IP for the eavesdropper’s channel. The asymptotic expressions for the OP and IP are also examined to give some insight into the system configuration under consideration. In order to verify the derived expressions, Monte-Carlo simulation is adopted;
- The suggested DNN performs almost as well as the simulation while drastically lowering the computing complexity. In comparison to existing machine learning-based regression models for OP/IP prediction, our suggested DNN technique has the lowest root mean square error (RMSE) and takes the shortest time to execute. When system attributes and channel circumstances vary, the data rate of the considered system can be customized based on the estimated OP/IP.

#### 1.3. Organization

## 2. System Model

#### 2.1. Energy Harvesting Model

#### 2.2. Fading Channel Model

#### 2.3. Transmission Model

## 3. Performance Analysis

#### 3.1. Outage Probability Analysis

#### 3.2. Intercept Probability Analysis

## 4. Asymptotic Analysis

#### 4.1. Op Analysis

#### 4.2. Ip Asymptotic Analysis

## 5. Dnn Network

#### 5.1. The DNN Design Description

- Data is sent to the input layer so that the DNN model may determine how the system parameters relate to the relevant OP/IP. The number of neurons in the input layer is, therefore, equal to the number of parameters and does not serve as an activation function;
- The number of hidden layers primarily determines the relationship between the input and output data. In order to accurately calculate the relationship, each connection in each hidden neuron has a separate weight and bias. In order to enhance computational effectiveness, each hidden neuron also has a nonlinear activation function;
- The output layer combines the findings of various hidden layers to predict OP/IP. As a result, there is just one neuron in the output layer. The neuron in the output layer lacks an activation function, much like the input layer.

#### 5.2. Dataset Setup

## 6. Numerical Results

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 4.**TheOP versus $\Psi $(dB) when varying ${\gamma}_{th}$ with $\eta =0.8$, ${\lambda}_{RR}=2$, and $\rho =0.25$.

**Figure 5.**TheIP versus $\Psi $(dB) when varying ${\gamma}_{th}$ with $\eta =0.8$, ${\lambda}_{RR}=2$, and $\rho =0.25$.

**Figure 6.**The OP versus ${\lambda}_{RR}$ when varying $\eta $ with $\rho =0.5$, ${\gamma}_{th}=1$, and $\Psi =5$(dB).

**Figure 7.**The IP versus ${\lambda}_{RR}$ when varying $\eta $ with $\rho =0.5$, ${\gamma}_{th}=1$, and $\Psi =5$(dB).

**Figure 8.**TheOP versus $\Psi $(dB) when varying $\rho $ with $\eta =0.8$, ${\lambda}_{RR}=2$, and ${\gamma}_{th}=1$.

**Figure 9.**TheIP versus $\Psi $(dB) when varying $\rho $ with $\eta =0.8$, ${\lambda}_{RR}=2$, and ${\gamma}_{th}=1$.

**Figure 10.**TheOP versus $\rho $ when varying ${\gamma}_{th}$ with $\eta =0.8$, ${\lambda}_{RR}=2$, and ${\Psi}_{th}=5$(dB).

**Figure 11.**The IP versus $\rho $ when varying ${\gamma}_{th}$ with $\eta =0.8$, ${\lambda}_{RR}=2$, and ${\Psi}_{th}=5$(dB).

Notation | Definition |
---|---|

${P}_{\mathrm{S}}$ | The transmit power at S |

${P}_{\mathrm{R}}$ | The transmit power at R |

${x}_{\mathrm{S}}$ | The transmit signal at S with $\mathbb{E}\left\{{x}_{\mathrm{S}}^{2}\right\}={P}_{\mathrm{S}}$ |

${x}_{\mathrm{R}}$ | The transmit signal at R with $\mathbb{E}\left\{{x}_{\mathrm{R}}^{2}\right\}={P}_{\mathrm{R}}$ |

$\eta $ | The conversion efficiency with $0<\eta \u2a7d1$ |

$\rho $ | The PS ratio with $0<\rho <1$ |

${\mathrm{R}}_{\mathrm{th}}$ | The target rate |

${n}_{\mathrm{R}}$, ${n}_{\mathrm{D}}^{1},{n}_{\mathrm{D}}^{2}$, ${n}_{\mathrm{E}}^{1}$, ${n}_{\mathrm{E}}^{2}$ | The AWGN with variance ${N}_{0}$ |

$\omega $ | The path loss exponent |

${d}_{\mathrm{SD}}$ | The distance from S to D |

${d}_{\mathrm{SR}}$ | The distance from S to R |

${d}_{\mathrm{SE}}$ | The distance from S to E |

${d}_{\mathrm{RD}}$ | The distance from R to D |

${d}_{\mathrm{RE}}$ | The distance from R to E |

$\mathbb{E}\left\{\u2022\right\}$ | The expectation operator |

${K}_{\nu}\left(\u2022\right)$ | The modified Bessel function of the second kind with $\nu $-th order: |

Input | Value | Input | Value |
---|---|---|---|

$\omega $ | 2 | ${\lambda}_{\mathrm{RR}}$ | [2,4] |

${d}_{\mathrm{SD}}$ | 1.5 | $\eta $ | 0.8 |

${d}_{\mathrm{SR}}$ | 1 | $\rho $ | 0.25 |

${d}_{\mathrm{RD}}$ | 0.5 | $\gamma $ | [0.5,1] |

${d}_{\mathrm{RE}}$ | 1 | $\Psi $ | [−5,25] |

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## Share and Cite

**MDPI and ACS Style**

Nguyen, T.N.; Minh, B.V.; Tran, D.-H.; Le, T.-L.; Le, A.-T.; Nguyen, Q.-S.; Lee, B.M.
Security–Reliability Analysis of AF Full-Duplex Relay Networks Using Self-Energy Recycling and Deep Neural Networks. *Sensors* **2023**, *23*, 7618.
https://doi.org/10.3390/s23177618

**AMA Style**

Nguyen TN, Minh BV, Tran D-H, Le T-L, Le A-T, Nguyen Q-S, Lee BM.
Security–Reliability Analysis of AF Full-Duplex Relay Networks Using Self-Energy Recycling and Deep Neural Networks. *Sensors*. 2023; 23(17):7618.
https://doi.org/10.3390/s23177618

**Chicago/Turabian Style**

Nguyen, Tan N., Bui Vu Minh, Dinh-Hieu Tran, Thanh-Lanh Le, Anh-Tu Le, Quang-Sang Nguyen, and Byung Moo Lee.
2023. "Security–Reliability Analysis of AF Full-Duplex Relay Networks Using Self-Energy Recycling and Deep Neural Networks" *Sensors* 23, no. 17: 7618.
https://doi.org/10.3390/s23177618