Partial transmit sequence based PAPR reduction with GA and PSO optimization techniques

Prasad, Sanjana; Ramesh, J.

doi:10.1109/iciiecs.2017.8276004

Cited by 4 publications

(8 citation statements)

References 8 publications

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“…This class of technology is widely applied with a focus on reducing the probability of peak occurrences. The most commonly used probabilistic class techniques are the Partial Transmit Sequence (PTS) method [7][8][9][10][11][12][13] and Selective Mapping (SLM) algorithm [14][15][16][17][18]. The disadvantage of the probabilistic class technique is that its computational complexity is high, requiring multiple inverse fast Fourier transform (IFFT) operations and calculation of the PAPR.…”

Section: ) Coding Class Techniquementioning

confidence: 99%

“…By continuously updating each individual in the population, individuals move towards the optimal solution, ultimately converging the entire population near the optimal solution. This optimization process replaces the exhaustive enumeration procedure in the traditional PTS, which can be a suitable solution to the problem of high computational complexity owing to searching for the phase factor in the PTS algorithm [14,15]. However, conventional DPSO algorithms often experience premature convergence, becoming trapped in local optima, and mistakenly considering them as global optima.…”

Section: ) Coding Class Techniquementioning

confidence: 99%

“…enumeration procedure in the traditional PTS, which can be a suitable solution to the problem of high computational complexity owing to searching for the phase factor in the PTS algorithm [14,15]. However, conventional DPSO algorithms often experience premature convergence, becoming trapped in local optima, and mistakenly considering them as global optima.…”

Section: Ofdm Fundamentals and The Peak-to-average Ratio Problemmentioning

confidence: 99%

See 2 more Smart Citations

Improved Algorithm of Partial Transmit Sequence Based on Discrete Particle Swarm Optimization

Wang,

Chen,

Dai

et al. 2023

Mathematics

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Orthogonal frequency division multiplexing (OFDM) in 5G has many advantages; however, one of the disadvantages is that the superposition of a large number of subcarriers leads to a high peak-to-average power ratio (PAPR) of the transmit signal. A high PAPR results in high-power amplifier distortion and performance degradation. The partial transmit sequence (PTS) algorithm is commonly used for PAPR reduction. It enumerates all combinations of phase factors, weighs the signal using each phase factor combination, and finds the set of phase factors that minimizes the PAPR value of the OFDM signal. The advantage of the PTS is that it determines the optimal solution through enumeration; however, its major drawback is the higher complexity caused by the use of enumeration. Some studies have introduced the discrete particle swarm optimization (DPSO) algorithm instead of enumeration to determine the optimal solution of the PTS algorithm. As an excellent optimization method, the DPSO algorithm represents each individual as a solution during the optimization. Through iterative updates of the initial population, individuals in the population continuously move closer to the optimal solution. This approach significantly reduces complexity compared with the exhaustive enumeration used in the traditional PTS algorithm. However, the disadvantage of the general DPSO algorithm is that it can result in premature and early convergence, which leads to degradation of the PAPR reduction performance. In this study, we propose an improved method based on the general DPSO-based PTS algorithm, and the improved algorithm MDPSO-PTS adopts dynamic time-varying learning factors, which can find the optimal combination of phase factors more efficiently. The MDPSO-PTS algorithm expands the search space when seeking the optimal combination of phase factors. This avoids the drawback of premature convergence commonly observed in general DPSO-PTS algorithms, preventing early consideration of local optima as global optima. A comparative simulation of the improved MDPSO-PTS algorithm with the general DPSO-PTS algorithm shows that the improved algorithm has stronger PAPR reduction, whereas the complexity remains basically unchanged. A comparative simulation with the traditional PTS algorithm shows a significant reduction in complexity, with only a slight, acceptable loss of reduction performance.

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Section: ) Coding Class Techniquementioning

confidence: 99%

Section: ) Coding Class Techniquementioning

confidence: 99%

Section: Ofdm Fundamentals and The Peak-to-average Ratio Problemmentioning

confidence: 99%

See 1 more Smart Citation

Improved Algorithm of Partial Transmit Sequence Based on Discrete Particle Swarm Optimization

Wang,

Chen,

Dai

et al. 2023

Mathematics

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show abstract

“…For that many optimisation methods have been proposed in recent years for optimal rotation phase factors search to get the required PAPR reduction performance with low computational complexity. The most used optimisation techniques are the particle swarm optimisation (PSO) [16,17], artificial bee colony (ABC) [18,19], and electromagnetism-like algorithm (EM) [20].…”

Section: Introductionmentioning

confidence: 99%

PTS scheme based on MCAKM for peak‐to‐average power ratio reduction in OFDM systems

Ali

Hamza

2020

IET Communications

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“…By using optimization techniques number of required searches can be reduced. Some of the excessively used methods are Genetic Algorithm (GA) [24,25], Particle Swarm Optimization (PSO) [26], Artificial Bee Colony (ABC) [27,28], Biogeography Based Optimization (BBO) [29] and differential evolution (DE) [30].…”

Section: Introductionmentioning

confidence: 99%

PAPR analysis of OFDM system using AI based multiple signal representation methods

2019

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OFDM (orthogonal frequency division multiplexing) is widely used in 4 th generation applications owing to its robustness in fading environments. The major issues with OFDM systems is the high PAPR (peak-to-average power ratio) of the transmitted signals, it leads to in and out of band distortion. SLM (selective mapping) and PTS (partial transmit sequence) are two key methods for PAPR reduction. Both the methods require exhaustive searching of phase factors to optimize the PAPR, these searches lead to high computational complexity. This paper discusses using optimization based PAPR reduction methods which an be used with PTS for the reduction of computational complexity and search space. In this paper we have analyzed PTS and SLM with particle swarm optimization (PSO), Artificial Bee Colony (ABC) and differential evolution (DE). PAPR and BER (bit error rate) comparison is done for both the cases.

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Partial transmit sequence based PAPR reduction with GA and PSO optimization techniques

Cited by 4 publications

References 8 publications

Improved Algorithm of Partial Transmit Sequence Based on Discrete Particle Swarm Optimization

Improved Algorithm of Partial Transmit Sequence Based on Discrete Particle Swarm Optimization

PTS scheme based on MCAKM for peak‐to‐average power ratio reduction in OFDM systems

PAPR analysis of OFDM system using AI based multiple signal representation methods

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