Effective PAPR reduction in SCFDM‐based massive MIMO system using binary crow search algorithm for visible light communication towards 5G networks

Sindhuja, Rajesh; Shankar, Arathi R

doi:10.1049/iet-com.2020.0334

Cited by 5 publications

(7 citation statements)

References 17 publications

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“…This section first proposes CUPSO-PTS with the IBO-MER scheme in 5.1, which applied the IBO-MER criteria to maximize HPA's energy efficiency under a fixed MER value to overcome drawbacks (1).…”

Section: Figurementioning

confidence: 99%

“…Multiple PAPR reduction algorithms have been proposed; probability algorithms such as selective mapping(SLM), partial transmit sequence(PTS), tone reservation(TR), tone injection(TI) are the most appropriate for high-order QAM multi-carrier schemes [15][16][17][18][19]. The PTS algorithm [20] has received extensive attention for its satisfactory PAPR suppression performance, compatibility with high-order quadrature amplitude modulation (QAM) communication systems, and ease of combining intelligent algorithms to enhance algorithm performance, but conventional PTS algorithm has serval disadvantages, such as high computational complexity [21] and poor optimizing precision [22].…”

Section: Introductionmentioning

confidence: 99%

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Continuous unconstrained PSO‐PTS strategy to maximize HPA energy efficiency for FBMC‐OQAM systems

Jin

et al. 2023

IET Communications

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In filter bank multi‐carrier (FBMC) with offset quadrature amplitude modulation (OQAM) systems, the major limitation of FBMC‐OQAM signaling is the high peak‐to‐average power ratio (PAPR). High PAPR results in increased hardware complexity and is also prone to non‐linear effects of high‐power amplifiers (HPA). The existing HPA energy‐efficient optimization strategies are constrained severely by the scarcity and heterogeneity of signal distribution in the degree of freedom. This paper theorizes the optimal FBMC‐OQAM signal distribution, which has a global and steady feature and evaluates the maximal HPA efficiency‐suited multi‐carrier modulation (MCM) signal. This paper first proposes that the Binary Particle Swarm Optimization based Partial Transmit Sequence (BPSO‐PTS) with the Input‐Back Off Modulation Error Ratio (IBO‐MER) scheme traces the signal distribution for maximal HPA efficiency to save supply‐side energy. This paper further proposes Continuous‐Unconstrained Particle Swarm Optimization based PTS(CUPSO‐PTS) with IBO‐MER to obtain the theoretical boundaries and drastically accelerate convergence in the continuous‐unconstrained searching space. To decrease the computational complexity of MER calculation, this paper proposes the CUPSO with IBO Signal‐to‐Distortion Ratio (IBO‐SDR), enabling it to meet more practical applications. The simulation results show that the proposed scheme outperforms conventional IBO and HPA efficiency schemes.

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Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Continuous unconstrained PSO‐PTS strategy to maximize HPA energy efficiency for FBMC‐OQAM systems

Jin

et al. 2023

IET Communications

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“…Due to high peak to average power ratio (PAPR), additive Gaussian white noise plays a great impact on visible light communication (VLC) with white light emitting diodes. To solve this problem in 5G networks, Sindhuja and Shankar employed selective mapping (SLM) to reduce PAPR and binary CSA optimizes subblock partition in SLM to improve its convergence rate on VLC system (Sindhuja and Shankar 2020 ). Industrial wireless sensor network (IWSN) is a new technology that greatly reduces control cost and advances production efficiency.…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

“…( 2020 ) Binary PSO Anchor Deployment Medium Low High Xiu-Wu et al. ( 2020 ) WPA Clustering Routing Medium Medium Low Sindhuja and Shankar ( 2020 ) Binary CSA 5G Fast High High Wang et al. ( 2011 ) Binary PSO Node Placement in IWSN Fast Medium High Moreno et al.…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

A survey on binary metaheuristic algorithms and their engineering applications

Pan

Snåšel

et al. 2022

Artif Intell Rev

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This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.

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“…A smaller AP value can increase the convergence of the algorithm. When the value of AP increases, the crow search algorithm will tend to search globally, and the probability of searching nearby areas to obtain a solution will decrease, but the diversity of the algorithm can be increased [33]. Since the position generated by the global search of the crow algorithm is completely random, in the crow algorithm, the crow j is found when the crow i is tracking (r i < AP).…”

Section: Crow Search Algorithm Optimized Particle Swarm Optimizamentioning

confidence: 99%

Improved Crow Search Algorithm Optimized Extreme Learning Machine Based on Classification Algorithm and Application

et al. 2021

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In view of the problems of the connection weights and thresholds of the extreme learning machine are randomly generated before training and remain unchanged during the training process, the number of hidden layer nodes is pre-allocated, and the hidden layer parameters are randomly selected. Too many hidden layer nodes not only make the network more complex but also reduce the generalization ability of the algorithm. Aiming at this problem, an improved crow search algorithm is proposed to optimize the extreme learning machine. Based on the analysis of the limitations of the original crow search algorithm, a particle swarm algorithm search strategy is proposed to enhance the global search capability. In the latter part of the algorithm iteration, Gaussian function is added, and the penalty coefficient of the function is used for local disturbance, gradually reducing the amplitude of the search trajectory, and then adaptively adjusting the parameters to avoid being attracted by local extremum. Finally, the improved crow search algorithm is used to optimize the hidden layer neurons and connection weights of the extreme learning machine neural network, so as to obtain accurate prediction results. Through function fitting, regression data set fitting and classification data set for classification experiment verification, the proposed algorithm has higher training speed and efficiency. At the same time, this method is not only significantly higher than the traditional ELM method, but also obtains a more compact network structure, which is an effective neural network optimization algorithm.

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Effective PAPR reduction in SCFDM‐based massive MIMO system using binary crow search algorithm for visible light communication towards 5G networks

Cited by 5 publications

References 17 publications

Continuous unconstrained PSO‐PTS strategy to maximize HPA energy efficiency for FBMC‐OQAM systems

Continuous unconstrained PSO‐PTS strategy to maximize HPA energy efficiency for FBMC‐OQAM systems

A survey on binary metaheuristic algorithms and their engineering applications

Improved Crow Search Algorithm Optimized Extreme Learning Machine Based on Classification Algorithm and Application

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