Boosted PTS Method with Mu-Law Companding Techniques for PAPR Reduction in OFDM Systems

Ibraheem, Zeyid T.; Ahmed, Kawakib K.; Fazea, Yousef; Madi, Mohamed T.; Mohammed, Fathey; Ali, Abdulrazzaq Qasem

doi:10.1007/s11277-021-09366-3

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…2) Mu-law Companding: Mu-law is a compression method that can be used to reduce PAPR [25], and PAPR reduction using the companding technique is discussed as follows.…”

Section: = 4 Wxy Wxy − Amentioning

confidence: 99%

Enhanced Artificial Bee Colony, Square Root Raised Cosine Precoding, and Mu law Compandor for Optimization of MIMO-OFDM System

Azeez¹,

Tarannum²

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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The efficiency and high-speed data transfer rate of the communication system are increased based on Orthogonal Frequency Division Multiplexing (OFDM). The existing research in OFDM involves applying optimization methods to improve the system's efficiency. The high Peak Average Power Ratio (PAPR) value is a major limitation in the OFDM system, and this provides distortion due to the non-linear High-Power Amplifier (HPA). Local optima trap and lower convergence are two main limitations in existing optimization methods. This research proposes Enhanced Artificial Bee Colony (ABC) optimization method with a precoding-compandor technique to increase the efficiency of the OFDM system. Enhanced ABC method is applied with Boltzmann search to increase the exploitation capacity of the optimization efficiency. The selective mapping technique is applied to transform the candidate signal in the system. The ABC method increases exploration, and Boltzmann search increases exploitation. The enhanced ABC method increases the exploitation process that helps to overcome local optima traps and lower convergence. Square Root Raised Cosine (SRRC) precoding and Mu law compandor techniques were applied to reduce the PAPR. The Discrete Cosine Transform (DCT) technique is applied for domain conversion in the OFDM system. The proposed method has a convergence rate of 6.4069, and the existing one has a 6.4033 convergence rate. The enhanced ABC method provides higher efficiency in the MIMO-OFDM system regarding Symbol Error Rate (SER), PAPR, and Bit Error Rate (BER).

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“…2) Mu-law Companding: Mu-law is a compression method that can be used to reduce PAPR [25], and PAPR reduction using the companding technique is discussed as follows.…”

Section: = 4 Wxy Wxy − Amentioning

confidence: 99%

Enhanced Artificial Bee Colony, Square Root Raised Cosine Precoding, and Mu law Compandor for Optimization of MIMO-OFDM System

Azeez¹,

Tarannum²

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…PTS distinguishes itself through its ability to achieve substantial reductions in the PAPR. However, this method has a trade-off: it is a complex and computationally demanding approach that requires multiple iterations to achieve its objective [ 54 , 55 ]. SLM represents another powerful strategy for reducing the PAPR, with the added benefit of being less susceptible to signal distortion.…”

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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“…Orthogonal frequency division multiplexing (OFDM) is one of the core technologies of the physical layer in 5G New Radio (5GNR). OFDM possesses the advantages of a high transmission rate and strong resistance to multipath interference and can be combined with large-scale MIMO [1,2]. OFDM, as a multicarrier modulation technique, has been chosen as the downlink waveform for 5G communication systems [3], and it is widely used.…”

Section: Introductionmentioning

confidence: 99%

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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Boosted PTS Method with Mu-Law Companding Techniques for PAPR Reduction in OFDM Systems

Cited by 8 publications

References 34 publications

Enhanced Artificial Bee Colony, Square Root Raised Cosine Precoding, and Mu law Compandor for Optimization of MIMO-OFDM System

Enhanced Artificial Bee Colony, Square Root Raised Cosine Precoding, and Mu law Compandor for Optimization of MIMO-OFDM System

A Survey of PAPR Techniques Based on Machine Learning

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

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