# Deep-Neural-Network-Based Receiver Design for Downlink Non-Orthogonal Multiple-Access Underwater Acoustic Communication

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## Abstract

**:**

## 1. Introduction

## 2. Downlink NOMA Underwater Acoustic Communication

## 3. Deep Neural Network

#### 1D Convolution Neural Network

## 4. Downlink Underwater Acoustic Communication Using a Deep Neural Network Receiver

## 5. Underwater Acoustic Communication Dataset Generation for DL DNN Receiver

## 6. Training and Analysis of DNN Receiver

## 7. Simulation Results and Discussion

^{−4}at 20 dB when there is a total of two users present in the system. Nevertheless, when the power of user 1 is reduced to 0.2 and 0.1, respectively, the BER performance deteriorates. Specifically, at a power level of 0.2, the BER reaches an approximately different power 0.033 at 20 dB, while, at a power level of 0.1, the BER is approximately 0.159. For user 2, the power ratio allocation is set to 0.9, 0.8, and 0.7, respectively. Figure 10b shows the average BER of user 2 at varying power levels. At a power ratio of 0.9, the average BER of the DNN-based receiver for user 2 is around 10

^{−3}at 12 dB and provides error-free transmission after 12 dB. However, as the power ratio of user 2 decreases to 0.8 and 0.7, the performance of the systems degrades and the average BER for 20 test channels is 0.01 and 0.09 at 20 dB for the 0.8 and 0.7 power ratio, respectively.

^{−2}as shown in Figure 12b. In contrast, at a power ratio 0.5, the average BER decreases to 10

^{−1}.

## 8. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Constellation diagram of different users when superimposed coding is employed by source station: (

**a**) user 1 BPSK modulation constellation; (

**b**) user 2 QPSK modulation constellation; (

**c**) resultant constellation obtained for two users after superimposed coding of QPSK-BSPK modulation; (

**d**) user 1 QPSK modulation constellation; (

**e**) user 2 QPSK modulation constellation; and (

**f**) resultant constellation obtained for two user after superimposed coding of QPSK-QSPK modulation.

**Figure 7.**Training underwater acoustic channel impulse response of various users: (

**a**) user 1 is 500 m away from the source node; (

**b**) user 2 is 1000 m away from the source node; and (

**c**) user 3 is at distance of 2000 m from the source node.

**Figure 8.**Training progress of 1D-CNN-based receiver for various users: (

**a**) training progress of user 1 at 500 m from source when two users are considered and using BPSK modulation; (

**b**) training progress of user 2 at 1000 m from source when two users are considered and using BPSK modulation; (

**c**) training progress of user 1 at 500 m from source when three users are considered and using BPSK modulation; (

**d**) training progress of user 2 at 2000 m from source when three users are considered and using BPSK modulation user; (

**e**) training progress of user 1 at 500 m from source when two users are considered and using QPSK modulation user; and (

**f**) training progress of user 2 at 1000 m from source when two users are considered and using QPSK modulation.

**Figure 9.**Testing underwater acoustic channel impulse response of various users: (

**a**) user 1 at a distance of 500 m from the source node; (

**b**) user 2 at a distance of 1000 m from the source node; and (

**c**) user 3 at distance of 2000 from the source node.

**Figure 10.**BER vs. SNR graphs of two users having varying power coefficients and using BPSK modulation: (

**a**) BER vs. SNR graph of user 1 at 500 m from the source node when allocated different power coefficient; and (

**b**) BER vs. SNR graph of user 2 at 1000 m from the source node when allocated different power.

**Figure 11.**BER vs. SNR graphs of two users having varying power coefficients and using QPSK modulation: (

**a**) BER vs. SNR graph of user 1 at 500 m from the source node when allocated different power coefficient; and (

**b**) BER vs. SNR graph of user 2 at 1000 m from the source node when allocated different power.

**Figure 12.**BER vs. SNR graphs of three users having varying power coefficients and using BPSK modulation: (

**a**) BER vs. SNR graph of user 1 at 500 m from the source node when allocated different power coefficient; and (

**b**) BER vs. SNR graph of user 3 at 2000 m from the source node when allocated different power.

**Figure 13.**BER vs. SNR comparison graph of proposed receiver and SIC receiver of user 1 in two-user system using BPSK modulation.

**Figure 14.**Comparison of BER vs. SNR of proposed receiver and SIC receiver of user 1 in two-user system using QPSK modulation.

**Figure 15.**Comparison of BER vs. SNR of proposed receiver and SIC receiver of user 1 in three-user system using BPSK modulation.

Parameters | Values |
---|---|

Source station depth | 5 m |

No. of users | 3 |

Depth of users | [20:2:50] m |

Distance of user 1 | [485 m, 515 m] |

Distance of user 2 | [985 m, 1015 m] |

Distance of user 3 | [1985 m, 2015 m] |

Transducer beam angle (°) | [−20, 20] |

Total depth | 50 m |

1D CNN Factors | User 1 | User 2 | User 3 |
---|---|---|---|

Sequence I/P layer(s) | 1 | 1 | 1 |

Total no. of convolution layer(s) | 4 | 4 | 4 |

Filter size of convolution layer(s) | [250, 120, 60, 30] | [250, 120, 60, 30] | [250, 120, 60, 30] |

Number of filters in convolution layer(s) | [150, 100, 30, 20] | [150, 100, 30, 20] | [150, 100, 30, 20] |

1D max-pooling layer(s) | 4 | 4 | 4 |

Number of flatten layer(s) | 1 | 1 | 1 |

Total number of global max-pooling layer(s) | 1 | 1 | 1 |

Number of neurons in fully connected layer | 120 | 120 | 120 |

Total batch size | 200 | 200 | 200 |

Hidden layer(s) activation function | ReLU | ReLU | ReLU |

Type of optimizer | Adam | Adam | Adam |

Output layer(s) activation function | Softmax | Softmax | Softmax |

Learning rate | 10^{−3} | 10^{−3} | 10^{−3} |

Total number of training channels | 50 | 50 | 50 |

BPSK training dataset (two-user case) | 50,000 | 50,000 | NA |

BPSK training dataset (three-user case) | 50,000 | 50,000 | 50,000 |

QPSK training dataset (two-user case) | 125,000 | 125,000 | NA |

Communication Parameters | Values |
---|---|

Carrier frequency | 12 kHz |

Sampling frequency | 100 kHz |

Modulation order(s) | [QPSK BPSK] |

Duration of symbol | 10 ms |

Power allocation coefficient (two-user case) | [0.7, 0.3] |

Power allocation coefficient (three-user case) | [0.6, 0.3, 0.1] |

Total number of symbols for testing | 50,000 |

Total number of testing channels | 20 |

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## Share and Cite

**MDPI and ACS Style**

Zuberi, H.H.; Liu, S.; Bilal, M.; Alharbi, A.; Jaffar, A.; Mohsan, S.A.H.; Miyajan, A.; Khan, M.A.
Deep-Neural-Network-Based Receiver Design for Downlink Non-Orthogonal Multiple-Access Underwater Acoustic Communication. *J. Mar. Sci. Eng.* **2023**, *11*, 2184.
https://doi.org/10.3390/jmse11112184

**AMA Style**

Zuberi HH, Liu S, Bilal M, Alharbi A, Jaffar A, Mohsan SAH, Miyajan A, Khan MA.
Deep-Neural-Network-Based Receiver Design for Downlink Non-Orthogonal Multiple-Access Underwater Acoustic Communication. *Journal of Marine Science and Engineering*. 2023; 11(11):2184.
https://doi.org/10.3390/jmse11112184

**Chicago/Turabian Style**

Zuberi, Habib Hussain, Songzuo Liu, Muhammad Bilal, Ayman Alharbi, Amar Jaffar, Syed Agha Hussnain Mohsan, Abdulaziz Miyajan, and Mohsin Abrar Khan.
2023. "Deep-Neural-Network-Based Receiver Design for Downlink Non-Orthogonal Multiple-Access Underwater Acoustic Communication" *Journal of Marine Science and Engineering* 11, no. 11: 2184.
https://doi.org/10.3390/jmse11112184