# Reducing PAPR with Low Complexity Filtered NOMA Using Novel Algorithm

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## Abstract

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## 1. Introduction

- The suggested scheme has ominously lowered the PAPR with low complexity.
- We have analyzed the PAPR, BER, Power Spectral Density (PSD), Power Performance, and Intricacy of the proposed algorithm for a high number of QAM schemes.

## 2. System Model

_{c}is the frequency of the carrier signal. The time domain NOMA with filter can be written as:

## 3. Simulation Results

#### 3.1. PAPR Performance of 16-QAM

^{−3}, the PAPR of conventional NOMA and F-NOMA is 9.4 dB and 8.2 dB, respectively. The SLM mode is applied to F-NOMA to lower the PAPR. It is shown that the PAPR is reduced to 2.4 dB, 4 dB, 4.8 dB, and 6.6 dB for SLM (S = 4 p = 4), (S = 2 p = 2), (S = 2 p = 2), and (S = 2 p = 4), respectively. Hence, it is concluded that SLM (S = 4 p = 4) outperforms conventional SLM. The PAPR analysis of 64-QAM for the proposed SLM algorithm is given in Figure 3. At the CCDF of 10

^{−3}, PAPR is lowered to 5.2 dB, 5.9 dB, 7 dB, and 8.2 dB for SLM (S = 4 p = 4, S = 4 p = 2, S = 2 p = 2, and S = 2 p = 4). Therefore, it is shown that SLM attained a 5.8 dB better performance as equated with the conventional NOMA framework. In Figure 4, the PAPR of 256-QAM for the F-NOMA waveform is shown. At 10

^{−3}CCDF, SLM (S = 4 p = 4) obtained a gain of 0.89, 1.8, and 4 dB, correspondingly.

#### 3.2. BER Analysis

^{−3}is achieved at the SNR of 5.3 dB for SLM (S = 4 p = 4), 6.5 dB for SLM (S = 4 p = 2), 7.7 dB for SLM (S = 2 p = 4), 8.6 dB for SLM (S = 2 p = 2), 10.6 dB for F-NOMA, and 12.1 dB for NOMA, respectively; hence, it is concluded that the BER is effectually enhanced for projected method.

#### 3.3. Power Spectral Density

#### 3.4. Complexity

#### 3.5. Power Saving Performance

## 4. Discussion and Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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References | PAPR Reduction (dB) at 10^{−3} | Remarks |
---|---|---|

[14] | 10 | A Block Coding (BC) scheme is used to reduce the PAPR of the F-OFDM. However, the complexity and applicability of the BC for advanced waveforms is not discussed in the presented work. |

[15] | 7 | The authors introduced a SLM for the OFDM structure. The increased number of subcarriers affects the amplifier efficacy. High BER is seen as one of the drawbacks of the suggested work. |

[16] | 7.2 | The authors proposed a SLM for 5G waveforms and OFDM. When applied to OFDM, the proposed algorithms performed well, but the computation requirements for 5G were high. |

[17] | 6.8 | The genetic algorithm-based PAPR schemes were introduced for advanced radio structure. The PAPR reduction is obtained at high BER. |

[18] | The article discussed the role of SLM in reducing the bandwidth leakage issue for the Universal Filter Multi Carrier (UFMC) waveform. The PAPR and BER analysis are not performed in the presented article. | |

[19] | 7.9 | The authors studied the throughput and amplifier performance of the UFMC structure. A significant reduction in power is obtained with high throughput. However, the computational factor is not discussed. |

Proposed Work | 2.4, 4.9 and 5.2 | A novel SLM-based F-NOMA is implemented, and significant power savings are obtained despite the low computation resource requirements. |

S.No. | Parameters |
---|---|

1 | Scheme: 16-QAM, 64-QAM and 256-QAM |

2 | Sub-block (S) = 2, 4 |

3 | Sub-carrier (N) = 64 |

4 | $\mathrm{PHYDYAS}\mathrm{filter}{P}^{s}=\left[2,4\right]$ |

S.NO. | Proposed Algorithm | Transmission Schemes | No of Additions | No of Multiplications |
---|---|---|---|---|

1 | SLM (S = 4 p = 4) | 16-QAM | 12,288 | 20,480 |

2 | SLM (S = 4 p = 4) | 64-QAM | 16,384 | 24,576 |

3 | SLM (S = 4 p = 4) | 256-QAM | 33,088 | 32,768 |

S.No | Proposed PAPR Algorithms | Original PAPR for 16-QAM (dB) | PAPR Reduction (dB) | Power Saving *100 (%) [16] |
---|---|---|---|---|

1 | SLM (S = 4 p = 4) | 8.2 | 2.4 | 70.70% |

2 | SLM (S = 4 p = 2) | 8.2 | 4 | 51.21% |

3 | SLM (S = 2 p = 4) | 8.2 | 4.8 | 41.46% |

4 | SLM (S = 2 p = 2) | 8.2 | 6.6 | 19.51% |

S.No | Proposed PAPR Algorithms | Original PAPR for 64-QAM (dB) | PAPR Reduction (dB) | Power Saving *100 (%) |
---|---|---|---|---|

1 | SLM (S = 4 p = 4) | 9.5 | 5.2 | 45.26% |

2 | SLM (S = 4 p = 2) | 9.5 | 5.9 | 37.89% |

3 | SLM (S = 2 p = 4) | 9.5 | 7 | 26.32% |

4 | SLM (S = 2 p = 2) | 9.5 | 8.2 | 13.68% |

S.No | Proposed PAPR Algorithms | Original PAPR for 256-QAM (dB) | PAPR Reduction (dB) | Power Saving *100 (%) |
---|---|---|---|---|

1 | SLM (S = 4 p = 4) | 10 | 4.9 | 51% |

2 | SLM (S = 4 p = 2) | 10 | 5.8 | 42% |

3 | SLM (S = 2 p = 4) | 10 | 6.7 | 33% |

4 | SLM (S = 2 p = 2) | 10 | 9 | 10% |

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**MDPI and ACS Style**

Kumar, A.; Rajagopal, K.; Gugapriya, G.; Sharma, H.; Gour, N.; Masud, M.; AlZain, M.A.; Alajmani, S.H.
Reducing PAPR with Low Complexity Filtered NOMA Using Novel Algorithm. *Sustainability* **2022**, *14*, 9631.
https://doi.org/10.3390/su14159631

**AMA Style**

Kumar A, Rajagopal K, Gugapriya G, Sharma H, Gour N, Masud M, AlZain MA, Alajmani SH.
Reducing PAPR with Low Complexity Filtered NOMA Using Novel Algorithm. *Sustainability*. 2022; 14(15):9631.
https://doi.org/10.3390/su14159631

**Chicago/Turabian Style**

Kumar, Arun, Karthikeyan Rajagopal, G. Gugapriya, Himanshu Sharma, Nidhi Gour, Mehedi Masud, Mohammed A. AlZain, and Samah H. Alajmani.
2022. "Reducing PAPR with Low Complexity Filtered NOMA Using Novel Algorithm" *Sustainability* 14, no. 15: 9631.
https://doi.org/10.3390/su14159631