Improving PAPR performance of filtered OFDM for 5G communications using PTS

Al‐Jawhar, Yasir Amer; Ramli, Khairun Nidzam; Taher, Montadar Abas; Shah, Nor Shahida Mohd; Mostafa, Salama A.; Khalaf, Bashar Ahmed

doi:10.4218/etrij.2019-0358

Cited by 27 publications

(10 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Al-Jawhar et al [10] presented a novel PTS model for decreasing the high PAPR of the filtered OFDM systems. It has analyzed different parameters namely frequency localization, bit error rate (BER), and computation difficulty under the existence of with and without PTS.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Based Stacked Sparse Autoencoder for PAPR Reduction in OFDM Systems

Jayamathi*¹,

Jayasankar²

2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Orthogonal frequency division multiplexing is one of the efficient and flexible modulation techniques, and which is considered as the central part of many wired and wireless standards. Orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) achieves maximum spectral efficiency and data rates for wireless mobile communication systems. Though it offers better quality of services, high peak-to-average power ratio (PAPR) is the major issue that needs to be resolved in the MIMO-OFDM system. Earlier studies have addressed the high PAPR of OFDM system using clipping, coding, selected mapping, tone injection, peak windowing, etc. Recently, deep learning (DL) models have exhibited improved performance on channel estimation, signal recognition, channel decoding, modulation identification, and end-to-end wireless system. In this view, this paper presents a new Hyperparameter Tuned Deep Learning based Stacked Sparse Autoencoder (HPT-SSAE) for PAPR Reduction Technique in OFDM system. The proposed model aims to substantially reduce the peaks in the OFDM signal. The presented HPT-SSAE model is utilized to adaptively create a peak-canceling signal based on the features of the input signal. In the HPT-SSAE model, the constellation mapping and demapping of symbols take place on every individual subcarrier adaptively using the SSAE model in such a way that bit error rate (BER) and the PAPR of the OFDM systems are cooperatively diminished. Besides, to enhance the performance of the SSAE model, the hyperparameter tuning process takes place using monarch butterfly optimization (MBO) algorithm. A comprehensive set of simulations were performed to highlight the supremacy of the HPT-SSAE model. The obtained experimental values showcased the betterment of the proposed model over the compared methods interms of bit error rate (BER), complementary cumulative distribution function (CCDF), and execution time.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Based Stacked Sparse Autoencoder for PAPR Reduction in OFDM Systems

Jayamathi*¹,

Jayasankar²

2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…This is because the filter causes the power distribution among the samples to be wider than that in the CP-OFDM system, which results in a decrease in the mean signal power, thereby causing degradation of the PAPR performance [7], [12]. Hence, CP-OFDM will exhibit the best PAPR performance when compared with the F-OFDM and UFMC systems [37], [41], [42].…”

Section: ) Filter Length and Paprmentioning

confidence: 99%

Performance Analysis of the Two-Piecewise Linear Companding Technique on Filtered-OFDM Systems

2021

View full text Add to dashboard Cite

Filtered orthogonal frequency division multiplexing (F-OFDM) is one of the most prominent waveform candidates when it comes to fifth-generation (5G) wireless communication and beyond. This paper analyzes the performance of the F-OFDM system in terms of the peak-to-average power ratio (PAPR) and bit error rate (BER). The F-OFDM system exhibits a high PAPR, which introduces earnest deterioration in its performance. Consequently, an efficient PAPR-reduction technique has been recommended to reduce the effect of the high value of PAPR of the F-OFDM system. The prospective two-piecewise companding (TPWC) scheme effectively reduces the peak power by analyzing large and small amplitudes individually, so its outcome has both piecewise linear and continuous characteristics. The performances of different PAPR reduction techniques are compared in terms of PAPR complementary cumulative distribution functions and BERs. It is shown that the proposed TPWC transform can significantly reduce PAPR with reduced computational complexity compared with conventional companding techniques.INDEX TERMS Peak-to-average power ratio (PAPR), Companding techniques, Filtered-OFDM, Two-Piece-wise linear companding technique, 5G

show abstract

“…Their studied scheme is centered on the permutation of PTS and BFOA and concluded that the presented arrangement efficiently lowers the PAPR and reduces the complexity of the framework. In [8], the authors compared the PAPR performance for filtered OFDM and conventional OFDM. The PTS is used in the F-OFDM waveform to lower the PAPR and it is seen that the F-OFDM outperforms the conventional OFDM waveform framework.…”

Section: Introductionmentioning

confidence: 99%

Reducing PAPR with Low Complexity Filtered NOMA Using Novel Algorithm

et al. 2022

View full text Add to dashboard Cite

Filtered Non-Orthogonal Multiple Access (F-NOMA) is a multi-carrier wave form and is considered a suitable contender for 5G radio. Peak to average power ratio (PAPR) is regarded as a major hurdle in the NOMA wave form because it hampers the efficiency of the power amplifier of the NOMA transmitter. In this study, a novel selective mapping (SLM) algorithm is used to minimize the PAPR of the NOMA. The conventional SLM increases the intricacy of the structure, and the projected SLM algorithm is applied to the transmitter part of the F-NOMA. Furthermore, we evaluate the performance of SLM on F-NOMA for 16, 64, and 256-Quadrature Amplitude Modulation (QAM) transmission methods. The parameters such as Bit Error Rate (BER), PAPR, power spectral density (PSD), and complexity are estimated and compared with different transmission patterns. The simulation outcomes of the work reveal that the optimal PAPR can be achieved by selecting the sub-block (S) and phase rotation elements (Ps). PAPR in F-NOMA achieves 1 dB gain in different QAM transmissions and its saving performance is 70.07%; however, complexity increases with an increase in modulation order.

show abstract

Improving PAPR performance of filtered OFDM for 5G communications using PTS

Cited by 27 publications

References 37 publications

Deep Learning Based Stacked Sparse Autoencoder for PAPR Reduction in OFDM Systems

Deep Learning Based Stacked Sparse Autoencoder for PAPR Reduction in OFDM Systems

Performance Analysis of the Two-Piecewise Linear Companding Technique on Filtered-OFDM Systems

Reducing PAPR with Low Complexity Filtered NOMA Using Novel Algorithm

Contact Info

Product

Resources

About