Multi-Objective Quantum Inspired Evolutionary SLM Scheme for PAPR Reduction in Multi-Carrier Modulation

Hou, Jun; Wang, Wei; Zhang, Yang; Liu, Xinyi; Xie, Ying

doi:10.1109/access.2020.2971633

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…Specifically, the study of [103] employed a quantum-inspired evolutionary algorithm that has a low computational complexity, and is capable of reducing the PAPR in orthogonal frequency-division multiplexing (OFDM) systems. The work of [104] expands the application of quantum-inspired evolutionary algorithms by utilizing them in multi-objective PAPR reduction.…”

Section: B Qml For Signal Intelligencementioning

confidence: 99%

Quantum Machine Learning for Next-G Wireless Communications: Fundamentals and the Path Ahead

Narottama,

Mohamed,

Aïssa

2023

IEEE Open J. Commun. Soc.

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A comprehensive coverage of the state-of-the-art in quantum machine learning (QML) methodologies, with a unique perspective on their applications for wireless communications, is presented. The paper begins by delving into the fundamental principles of quantum computing, and then goes through different operations and techniques that are involved in QML deployments. Subsequently, it provides an in-depth look at various methods peculiar to quantum computing, such as quantum search algorithms, and discusses their potentials towards maximizing the performance of wireless systems. The integration of quantum-based learning models into the existing machine learning methodologies, such as within the frameworks of unsupervised learning and reinforcement learning, are then examined. Taking the viewpoint of wireless communications, diverse studies in the literature that employ QML-based optimization methods are also highlighted. Finally, to ensure the applicability and feasibility of QML for optimizing wireless systems, potential solutions for deployment challenges are addressed.INDEX TERMS Next-generation wireless communications; Quantum machine learning; Optimization of wireless systems.

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Section: B Qml For Signal Intelligencementioning

confidence: 99%

Quantum Machine Learning for Next-G Wireless Communications: Fundamentals and the Path Ahead

Narottama,

Mohamed,

Aïssa

2023

IEEE Open J. Commun. Soc.

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“…These methods have been successfully used to solve combinatorial optimization problems, such as scheduling problems [42,43], load forecast [44], routing optimization [45], disease diagnosis [46], and optimal design [47]. In addition, algorithms based on QPSO play important roles in other multi-objective problems, including multi-carrier communication [48] and system control [49].…”

Section: Quantum Computingmentioning

confidence: 99%

Multi-Objective Quantum-Inspired Seagull Optimization Algorithm

Wang

Ahmad

et al. 2022

Electronics

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Objective solutions of multi-objective optimization problems (MOPs) are required to balance convergence and distribution to the Pareto front. This paper proposes a multi-objective quantum-inspired seagull optimization algorithm (MOQSOA) to optimize the convergence and distribution of solutions in multi-objective optimization problems. The proposed algorithm adopts opposite-based learning, the migration and attacking behavior of seagulls, grid ranking, and the superposition principles of quantum computing. To obtain a better initialized population in the absence of a priori knowledge, an opposite-based learning mechanism is used for initialization. The proposed algorithm uses nonlinear migration and attacking operation, simulating the behavior of seagulls for exploration and exploitation. Moreover, the real-coded quantum representation of the current optimal solution and quantum rotation gate are adopted to update the seagull population. In addition, a grid mechanism including global grid ranking and grid density ranking provides a criterion for leader selection and archive control. The experimental results of the IGD and Spacing metrics performed on ZDT, DTLZ, and UF test suites demonstrate the superiority of MOQSOA over NSGA-II, MOEA/D, MOPSO, IMMOEA, RVEA, and LMEA for enhancing the distribution and convergence performance of MOPs.

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“…Recently, many metaheuristic algorithms are used to reduce the SLM technique's computational complexity, including the artificial bee colony algorithm [34], the quantum inspired evolutionary algorithm [35], the migrating birds' optimization algorithm [36], and the firefly algorithm [37]. However, metaheuristic algorithms degrade the PAPR reduction performance, unless use the same number of IFFT processes as the conventional SLM.…”

Section: Introductionmentioning

confidence: 99%

Low-complexity selective mapping technique for PAPR reduction in downlink power domain OFDM-NOMA

Mounir

Youssef

Aboshosha

2023

EURASIP J. Adv. Signal Process.

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To support ultra-massive connectivity, non-orthogonal multiple access (NOMA) techniques are expected to be used in 6G instead of conventional orthogonal multiple access (OMA) techniques. Furthermore, given that orthogonal frequency division multiplexing (OFDM) was used by 4G and 5G, NOMA is expected to be combined with OFDM to mitigate frequency selectivity in multipath channels. However, after passing through nonlinear high power amplifiers (HPAs), OFDM-NOMA has reportedly suffered from nonlinear distortion due to the high peak-to-average power ratio (PAPR) problem, thus reducing users’ achievable data rates, sum rate capacity, and users’ bit error rate after going through nonlinear HPAs. Many PAPR reduction techniques have been introduced in the literature; however, high PAPR reduction gain comes at the expense of computational complexity. In this paper, a novel selective mapping (SLM) scheme for PAPR reduction in OFDM-NOMA systems is proposed. By utilizing the structure of the OFDM-NOMA transmitter, the proposed SLM scheme achieves the same performance as that of the conventional SLM scheme while requiring less computational complexity.

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Multi-Objective Quantum Inspired Evolutionary SLM Scheme for PAPR Reduction in Multi-Carrier Modulation

Cited by 10 publications

References 22 publications

Quantum Machine Learning for Next-G Wireless Communications: Fundamentals and the Path Ahead

Quantum Machine Learning for Next-G Wireless Communications: Fundamentals and the Path Ahead

Multi-Objective Quantum-Inspired Seagull Optimization Algorithm

Low-complexity selective mapping technique for PAPR reduction in downlink power domain OFDM-NOMA

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