A phase factor generation using RNNs deep learning algorithm-based PTS method for PAPR reduction of beyond 5G FBMC waveform
Filter Bank Multicarrier (FBMC) is considered one of the strong applicants for a radio system beyond the fifth generation (B5G) that improves spectral access and lowers interference. It utilizes a prototype filter for each sub-carrier, making it best for the beyond fifth generation (B5G) framework....
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Elsevier
2025-01-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824011955 |
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| author | Aziz Nanthaamornphong Nishant Gaur Lakshmana Phaneendra Maguluri Arun Kumar |
| author_facet | Aziz Nanthaamornphong Nishant Gaur Lakshmana Phaneendra Maguluri Arun Kumar |
| author_sort | Aziz Nanthaamornphong |
| collection | DOAJ |
| description | Filter Bank Multicarrier (FBMC) is considered one of the strong applicants for a radio system beyond the fifth generation (B5G) that improves spectral access and lowers interference. It utilizes a prototype filter for each sub-carrier, making it best for the beyond fifth generation (B5G) framework. The performance of the FBMC is hugely impacted by the high peak-to-average power ratio (PAPR), which lowers the effectiveness of the power amplifier (PA) used in the 5G-based FBMC waveform. The conventional partial transmission sequence (PTS) technique requires high computational complexity due to the need for multiple Inverse Fast Fourier Transforms (IFFTs) and phase optimization, which can increase processing time and system latency. This article proposes a hybrid method combining a partial transmission sequence and recurrent neural network (RNN) known as PTS-RNNs. RNNs improve the performance of the PTS by efficiently predicting optimal phase factors, reducing computational complexity, and lowering the PAPR of the FBMC waveform. The parameters such as PAPR, bit error rate (BER), and power spectral density (PSD) are estimated for 256 sub-carriers under the Rayleigh and Rician channels for FBMC and orthogonal frequency division multiplexing (OFDM). The experiment results reveal that the proposed PTS-RNNs method achieves an efficient 55.45 % and 67.56 % power saving performance for Rayleigh and Rician channels, with enhanced PSD performance while preserving the BER compared to the traditional selective mapping (SLM) and PTS methods. It is also noticeable that by adding more sub-blocks and phase parameters, PAPR can be further optimised. |
| format | Article |
| id | doaj-art-addc8cb0028844f69da9c8a9fdc9fe38 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-addc8cb0028844f69da9c8a9fdc9fe382025-01-09T06:13:25ZengElsevierAlexandria Engineering Journal1110-01682025-01-01110468478A phase factor generation using RNNs deep learning algorithm-based PTS method for PAPR reduction of beyond 5G FBMC waveformAziz Nanthaamornphong0Nishant Gaur1Lakshmana Phaneendra Maguluri2Arun Kumar3College of Computing, Prince of Songkla University, Phuket, ThailandDepartment of Physics, JECRC University, Jaipur, IndiaDepartment of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, IndiaDepartment of Electronics and Communication Engineering, New Horizon college of Engineering, Bengaluru, India; Corresponding author.Filter Bank Multicarrier (FBMC) is considered one of the strong applicants for a radio system beyond the fifth generation (B5G) that improves spectral access and lowers interference. It utilizes a prototype filter for each sub-carrier, making it best for the beyond fifth generation (B5G) framework. The performance of the FBMC is hugely impacted by the high peak-to-average power ratio (PAPR), which lowers the effectiveness of the power amplifier (PA) used in the 5G-based FBMC waveform. The conventional partial transmission sequence (PTS) technique requires high computational complexity due to the need for multiple Inverse Fast Fourier Transforms (IFFTs) and phase optimization, which can increase processing time and system latency. This article proposes a hybrid method combining a partial transmission sequence and recurrent neural network (RNN) known as PTS-RNNs. RNNs improve the performance of the PTS by efficiently predicting optimal phase factors, reducing computational complexity, and lowering the PAPR of the FBMC waveform. The parameters such as PAPR, bit error rate (BER), and power spectral density (PSD) are estimated for 256 sub-carriers under the Rayleigh and Rician channels for FBMC and orthogonal frequency division multiplexing (OFDM). The experiment results reveal that the proposed PTS-RNNs method achieves an efficient 55.45 % and 67.56 % power saving performance for Rayleigh and Rician channels, with enhanced PSD performance while preserving the BER compared to the traditional selective mapping (SLM) and PTS methods. It is also noticeable that by adding more sub-blocks and phase parameters, PAPR can be further optimised.http://www.sciencedirect.com/science/article/pii/S1110016824011955Beyond 5GFBMCPAPRPTS-RNNsBERPSD |
| spellingShingle | Aziz Nanthaamornphong Nishant Gaur Lakshmana Phaneendra Maguluri Arun Kumar A phase factor generation using RNNs deep learning algorithm-based PTS method for PAPR reduction of beyond 5G FBMC waveform Alexandria Engineering Journal Beyond 5G FBMC PAPR PTS-RNNs BER PSD |
| title | A phase factor generation using RNNs deep learning algorithm-based PTS method for PAPR reduction of beyond 5G FBMC waveform |
| title_full | A phase factor generation using RNNs deep learning algorithm-based PTS method for PAPR reduction of beyond 5G FBMC waveform |
| title_fullStr | A phase factor generation using RNNs deep learning algorithm-based PTS method for PAPR reduction of beyond 5G FBMC waveform |
| title_full_unstemmed | A phase factor generation using RNNs deep learning algorithm-based PTS method for PAPR reduction of beyond 5G FBMC waveform |
| title_short | A phase factor generation using RNNs deep learning algorithm-based PTS method for PAPR reduction of beyond 5G FBMC waveform |
| title_sort | phase factor generation using rnns deep learning algorithm based pts method for papr reduction of beyond 5g fbmc waveform |
| topic | Beyond 5G FBMC PAPR PTS-RNNs BER PSD |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824011955 |
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