# The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems

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## Abstract

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## Abstract

## 1. Introduction

## 2. Methods

#### 2.1. An Overview of the DCO-OFDM System Model

#### 2.2. The ESLM-AE PAPR Reduction Scheme

#### 2.2.1. Autoencoder Network

#### 2.2.2. Extended Selected Mapping Technique

## 3. Results and Discussion

#### 3.1. PAPR Comparison

#### 3.2. BER Analysis

## 4. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**An overview of the DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) system with the autoencoder network combined with extended selected mapping methods (ESLM-AE) structure, where S/P, P/S, and PD denote serial-to-parallel converter, parallel-to-serial converter, and photodetector, respectively.

**Figure 3.**Partial block diagram of an OFDM transmitter with the extended Selected Mapping (SLM) technique.

**Figure 4.**Complementary cumulative distribution function (CCDF) of peak to average power ratio (PAPR) comparison of ESLM-AE, Autoencoder, clipped DCO-OFDM, DCO-OFDM and SLM. The clipping ratios, γ, used for ESLM-AE and Autoencoder is 1.5.

**Figure 5.**Bit error rate (BER) comparison of the clipped DCO-OFDM, SLM, Autoencoder and the proposed scheme under the line-of-sight (LOS) channel.

**Figure 6.**BER comparison of the ESLM-AE and SLM under the Rician fading channel with additive white Gaussian noise (AWGN).

**Figure 7.**BER comparison of the clipped DCO-OFDM, SLM, Autoencoder and the proposed scheme under the DOW channel.

**Figure 8.**BER comparison of the clipped DCO-OFDM, SLM, Autoencoder and the proposed scheme under the DOW channel with/without ISI. The clipping ratios, γ, used for clipping, ESLM-AE and Autoencoder are 1.5.

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

Transmitter (TX) dimensions | |

Input | 128 |

Dense (ReLU) | 2048 |

Dense (ReLU) | 2048 |

Dense (ReLU) | 128 |

Receiver (RX) dimensions | |

Input | 128 |

Dense (ReLU) | 2048 |

Dense (ReLU) | 2048 |

Dense (sigmoid) | 128 |

Optimizer | SGD with Adam [40] |

Learning rate $\lambda $ | 0.0001 |

Batch size | 32 |

Weight parameter $\eta $ Dropout probability | 0.01 0.1 |

Number of subcarriers | 128 |

Length of cyclic prefix | 32 |

Results | Average PAPR | BER |
---|---|---|

Training set | 2.0198 | 0.0003976 |

Validation set | 2.0499 | 0.0004077 |

Test set | 2.0439 | 0.0004059 |

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## Share and Cite

**MDPI and ACS Style**

Hao, L.; Wang, D.; Tao, Y.; Cheng, W.; Li, J.; Liu, Z. The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems. *Appl. Sci.* **2019**, *9*, 852.
https://doi.org/10.3390/app9050852

**AMA Style**

Hao L, Wang D, Tao Y, Cheng W, Li J, Liu Z. The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems. *Applied Sciences*. 2019; 9(5):852.
https://doi.org/10.3390/app9050852

**Chicago/Turabian Style**

Hao, Lili, Dongyi Wang, Yang Tao, Wenyong Cheng, Jing Li, and Zehan Liu. 2019. "The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems" *Applied Sciences* 9, no. 5: 852.
https://doi.org/10.3390/app9050852