An artificial bee colony-based SLM scheme for PAPR reduction in OFDM systems

Cheng, Xing; Liu, Dejun; Feng, Shuo; Fang, Huafeng; Liu, Rachel

doi:10.1109/ciapp.2017.8167258

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…FWA is used to find the optimal combination of phase factors to reduce PAPR. The OFDM system was simulated with 64 subcarriers, in which 4 sub-blocks are employed, and the PTS, selected mapping (SLM) [58] and GA used W = 4 {1, À1, j, Àj} phase weighting factors to optimize the PAPR of the modulated OFDM symbol, while others algorithms chose randomly the four phases within the interval W = [0, 2π]. For the FWA, the parameters were chosen as described in [52], the FWA worked quite well at the setting: m ¼ 5, Â ¼ 40, N = 5, m = 50, a = 0.04 and b = 0.8 [57].…”

Section: The Position Of Sparks Is Controlled By the Explosion Amplit...mentioning

confidence: 99%

A Comparison Study of PAPR Reduction in OFDM Systems Based on Swarm Intelligence Algorithms

Amhaimar¹,

Yaakoubi²,

Mohamed³

et al. 2023

Search Algorithm - Essence of Optimization

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Optimization algorithms have been one of the most important research topics in Computational Intelligence Community. They are widely utilized mathematical functions that solve optimization problems in a variety of purposes via the maximization or minimization of a function. The swarm intelligence (SI) optimization algorithms are an active branch of Evolutionary Computation, they are increasingly becoming one of the hottest and most important paradigms, several algorithms were proposed for tackling optimization problems. The most respected and popular SI algorithms are Ant colony optimization (ACO) and particle swarm optimization (PSO). Fireworks Algorithm (FWA) is a novel swarm intelligence algorithm, which seems effective at finding a good enough solution of a complex optimization problem. In this chapter we proposed a comparison study to reduce the high PAPR (Peak-to-Average Power Ratio) in OFDM systems based on the swarm intelligence algorithms like simulated annealing (SA), particle swarm optimization (PSO), fireworks algorithm (FWA), and genetic algorithm (GA). It turns out from the results that some algorithms find a good enough solutions and clearly outperform the others candidates in both convergence speed and global solution accuracy.

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Section: The Position Of Sparks Is Controlled By the Explosion Amplit...mentioning

confidence: 99%

A Comparison Study of PAPR Reduction in OFDM Systems Based on Swarm Intelligence Algorithms

Amhaimar¹,

Yaakoubi²,

Mohamed³

et al. 2023

Search Algorithm - Essence of Optimization

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“…To address this challenge, various approaches have been proposed to mitigate high PAPR in both OFDM [6]-[9] and MIMO-OFDM signals [10]- [12]. These approaches encompass techniques such as clipping [13], clipping and filtering [14], tone injection [15], coding [16], peak windowing [17], selected mapping (SLM) [18], [19], and partial transmit sequence (PTS) [20]- [22].…”

Section: Introductionmentioning

confidence: 99%

Enhancing PAPR reduction efficiency in MIMO-OFDM systems via selective mapping and metaheuristic algorithms

Amhaimar,

Nadir,

Abdellah

et al. 2024

IJEECS

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The relentless evolution of communication systems, driven by the demands of 5G and the impending 6G networks, necessitates heightened data rates and spectral efficiency. orthogonal frequency division multiplexing (OFDM), a form of multicarrier modulation employed in multi-input multi-output (MIMO) systems, stands as a pivotal technology. Yet, OFDM grapples with challenges, notably the peak-to-average power ratio (PAPR) issue. Selective mapping (SLM) has been a favored technique for mitigating PAPR in OFDM, albeit challenged by computational complexities in its pursuit of discovering optimal phase factors. This paper pioneers a transformative approach by integrating metaheuristic algorithms genetic algorithm (GA), particle swarm optimization (PSO), and the innovative fireworks algorithm (FWA) into SLM for PAPR reduction while minimizing computational complexity. Simulation results not only affirm the efficacy of SLM-based techniques but also spotlight the potential of metaheuristic algorithms in addressing PAPR challenges in modern communication systems. The study transcends single-antenna systems, extending to MIMO-OFDM systems based on WiMAX standards, validating the efficacy of these techniques in multi-antenna configurations. Crucially, the FWA, proposed for the first time in this paper, emerges as a robust candidate, striking an enviable balance between computational efficiency and performance, achieving a notable PAPR reduction with a favorable search number.

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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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An artificial bee colony-based SLM scheme for PAPR reduction in OFDM systems

Cited by 9 publications

References 21 publications

A Comparison Study of PAPR Reduction in OFDM Systems Based on Swarm Intelligence Algorithms

A Comparison Study of PAPR Reduction in OFDM Systems Based on Swarm Intelligence Algorithms

Enhancing PAPR reduction efficiency in MIMO-OFDM systems via selective mapping and metaheuristic algorithms

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

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