PAPR Reduction in OFDM Systems Using Modified SLM with Different Phase Sequences

Prasad, Sanjana; Ramesh, J.

doi:10.1007/s11277-019-06763-7

Cited by 35 publications

(9 citation statements)

References 11 publications

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“…TD-C-SLM generates the signal candidates by summing the original OFDM signal another work done by Pamungkasari et al [33] proposed the effects of cyclic shift resolution of the OFDM system with C-SLM-DC-MF scheme on the system performance, in terms of PAPR reduction, accuracy, and bit error rate (BER). Prasad and Ramesh [34] analyzed various phase sequences such as Riemann, Centering, Centred Riemann and New Centred to Modified SLM technique and their effect on PAPR reduction. Valluri and Mani [35] utilized the concepts of conversion matrices and linearity to achieve low complexity with respect to the modulation matrices in the conventional SLM method.…”

Section: Related Workmentioning

confidence: 99%

An efficient SLM technique based on chaotic biogeography-based optimization algorithm for PAPR reduction in GFDM waveform

Kumar

Kumar²,

Rose

2022

Automatika

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High data rates, extremely low power consumption, and minimal end-to-end latency are considered to be mandatory requirements for 5G wireless networks. Rapid improvements in design and performance of 5G physical layer waveforms have become necessary. The drawback of Orthogonal Frequency Division Multiplexing (OFDM) is high PAPR, that causes signal distortion, which reduces system efficiency. Generalized frequency division multiplexing (GFDM) is a promising non-orthogonal multicarrier transmission scheme, which has recently received a great deal of attention towards future fifth generation (5G) wireless networks. It overcomes the limitations of orthogonal frequency division multiplexing (OFDM), while preserving most of the advantages of it. Selective Level mapping (SLM) is one of the PAPR reduction techniques, that uses the phase shift technology. In this paper, SLM based on Chaotic Biogeography Based Optimization (CBBO) algorithm is proposed to offer an efficient solution to the problem of high PAPR, existing in the GFDM waveforms. Experimental results prove that, the proposed CBBO-SLM technique provides significant improvement in terms of PAPR reduction, as compared to the conventional SLM methods, such as conventional GFDM and OFDM-SLM. The proposed novel scheme is most suitable for QAM and QPSK applications, as it provides good PAPR reduction performance, at lower computational complexity.

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Section: Related Workmentioning

confidence: 99%

An efficient SLM technique based on chaotic biogeography-based optimization algorithm for PAPR reduction in GFDM waveform

Kumar

Kumar²,

Rose

2022

Automatika

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“…Prasad and Jayabalan [24] have introduced selective mapping to minimize the PAPR ratio. Here, the variation of the PAPR ratio during the communication process has been measured by changing the phase sequence based on the communication range.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Deep Neural Based Peak-To-Average Power Ratio Mitigated System for Multi User OFDM Channel

Reddy¹,

Sheeba

2022

Preprint

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An Orthogonal Frequency Division Multiplexing (OFDM) signal having a large Peak-to-Average Power ratio (PAPR) can cause noise signals and power degradation. To remove the high PAPR in OFDM signals, the proposed Strawberry-based Recurrent Neural Framework (SbRNF) model is proposed. While transmitting the signals in the OFDM channel, the rate of PAPR was high due to more subcarriers. Basically, more subcarriers are needed to convey the OFDM channel's signals. Our proposed model made the reduction of PAPR in the OFDM channel easier. The result of the proposed model (SbRNF) was determined, and signal-to-noise ratio (SNR), Throughput, and Bit Error Rate (BER) were compared with other existing methodologies. Here, the presented model is executed in the MATLAB platform and shows that SbRNF has a low Bit Error Rate (BER), high throughput value, and Cumulative Distribution Function (CCDF) performance in the presence of high PAPR. While comparing the existing models with SbRNF provides better PAPR reduction in the signal transmission.

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“…SLM represents another powerful strategy for reducing the PAPR, with the added benefit of being less susceptible to signal distortion. However, it places significant demands on processing power and is sensitive to nonlinear channel conditions [ 56 , 57 ]. Interleaving exhibits effectiveness in PAPR reduction, particularly when used in conjunction with other techniques.…”

Section: Introductionmentioning

confidence: 99%

A Survey of PAPR Techniques Based on Machine Learning

da Silva,

Souto,

Souza

et al. 2024

Sensors

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Orthogonal Frequency Division Multiplexing (OFDM) is the modulation technology used in Fourth Generation (4G) and Fifth Generation (5G) wireless communication systems, and it will likely be essential to Sixth Generation (6G) wireless communication systems. However, OFDM introduces a high Peak to Average Power Ratio (PAPR) in the time domain due to constructive interference among multiple subcarriers, increasing the complexity and cost of the amplifiers and, consequently, the cost and complexity of 6G networks. Therefore, the development of new solutions to reduce the PAPR in OFDM systems is crucial to 6G networks. The application of Machine Learning (ML) has emerged as a promising avenue for tackling PAPR issues. Along this line, this paper presents a comprehensive review of PAPR optimization techniques with a focus on ML approaches. From this survey, it becomes clear that ML solutions offer customized optimization, effective search space navigation, and real-time adaptability. In light of the demands of evolving 6G networks, integration of ML is a necessity to propel advancements and meet increasing prerequisites. This integration not only presents possibilities for PAPR reduction but also calls for continued exploration to harness its potential and ensure efficient and reliable communication within 6G networks.

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PAPR Reduction in OFDM Systems Using Modified SLM with Different Phase Sequences

Cited by 35 publications

References 11 publications

An efficient SLM technique based on chaotic biogeography-based optimization algorithm for PAPR reduction in GFDM waveform

An efficient SLM technique based on chaotic biogeography-based optimization algorithm for PAPR reduction in GFDM waveform

An Intelligent Deep Neural Based Peak-To-Average Power Ratio Mitigated System for Multi User OFDM Channel

A Survey of PAPR Techniques Based on Machine Learning

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