# Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks

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## Abstract

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## 1. Introduction

- We employ the deep learning-based technique for secure MIMO communications in heterogeneous networks, which can exploit the benefits of CNN learning model to produce more accurate CSI and meanwhile reduce the bit error rate (BER) of the receiver.
- We provide the detailed framework of deep learning-based detectors, where imperfect CSI as well as the original messages or ideal CSI are included in the training set, and can be used in different application scenarios.
- We present simulation results for deep learning-based detectors in heterogeneous networks. With the help of the CNN technique, the proposed detectors show obvious performance gain over the MLD with acceptable computational cost.

## 2. Related Work

## 3. System Model

## 4. Deep CNN-Based Detector

#### 4.1. DCNN Type-I: Training with Accurate CSI

#### 4.2. DCNN Type-II: Training with Original Message

## 5. Simulation Results

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**MDPI and ACS Style**

Deng, D.; Li, X.; Zhao, M.; Rabie, K.M.; Kharel, R.
Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks. *Sensors* **2020**, *20*, 1730.
https://doi.org/10.3390/s20061730

**AMA Style**

Deng D, Li X, Zhao M, Rabie KM, Kharel R.
Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks. *Sensors*. 2020; 20(6):1730.
https://doi.org/10.3390/s20061730

**Chicago/Turabian Style**

Deng, Dan, Xingwang Li, Ming Zhao, Khaled M. Rabie, and Rupak Kharel.
2020. "Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks" *Sensors* 20, no. 6: 1730.
https://doi.org/10.3390/s20061730