Differential evolution-based optimal power allocation scheme for NOMA-VLC systems

Dong, Zanyang; Shang, Tao; Li, Qian; Tang, Tang

doi:10.1364/oe.397761

Cited by 20 publications

(6 citation statements)

References 15 publications

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“…Verify the superiority of two proposed power allocation schemes over the conventional FPA and GRPA in terms of sum rate and user fairness using simulation [68]…”

Section: Sum Ratementioning

confidence: 99%

A Survey of NOMA for VLC Systems: Research Challenges and Future Trends

Sadat

Abaza

Mansour

et al. 2022

Sensors

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Visible light communication (VLC) has become a promising technology for high data rate communications and an attractive complementary to conventional radio frequency (RF) communication. VLC is a secure, energy efficient and cost-effective technology that exploits the existing infrastructure, particularly in indoor environments, for wireless data transmission. Nevertheless, the main limitation of developing high data rate VLC links is the narrow modulation bandwidth of light-emitting diodes (LEDs), which is in the megahertz range. The power domain nonorthogonal multiple access (PD-NOMA) scheme is envisioned to address several challenges in VLC systems. In this paper, we present a detailed overview of PD-NOMA based VLC systems. Moreover, we introduce insights on some PD-NOMA VLC system constraints and challenges such as power allocation, clipping effect, MIMO and security. Finally, we provide open research problems as well as possible directions for future research to pave the way for the implementation of PD-NOMA VLC systems.

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“…Verify the superiority of two proposed power allocation schemes over the conventional FPA and GRPA in terms of sum rate and user fairness using simulation [68]…”

Section: Sum Ratementioning

confidence: 99%

A Survey of NOMA for VLC Systems: Research Challenges and Future Trends

Sadat

Abaza

Mansour

et al. 2022

Sensors

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show abstract

Section: Introductionmentioning

confidence: 99%

“…DPA based metaheuristic optimization methods are iterative methods that increase implementation complexity as well as conventional DPA numerical search methods. For instance, DPA based DE 49 has been compared with GRPA, DPA based PSO 50 has been compared with NGDPA and GRPA, while DPA based GA has been compared with FTPA 51 and GRPA. 52 As can be seen from the related works given above, no comprehensive and realistic comparison of various DPA strategic design methods has yet been published, taking into account the BER, sum-rate capacity, fairness, and distance between the users.…”

Section: Introductionmentioning

confidence: 99%

On The Selection of Power Allocation Strategy in Power Domain Non‐Orthogonal Multiple Access (PD‐NOMA) for 6G and Beyond

Mounir

Mashade

Aboshosha

2021

Trans Emerging Tel Tech

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Non-orthogonal multiple access (NOMA) is an attractive candidate for 6G networks to support ultra-massive machine-type communications (umMTC). Power domain NOMA (PD-NOMA) is the simplest type of NOMA, which assigns a different power level to each user. Power allocation in PD-NOMA can be classified into fixed/dynamic power allocation (FPA/DPA). FPA is simple, but DPA is more suitable for the mobile environment than FPA. However, finding optimum power per each user in DPA is extremely complex. Fortunately, many DPA strategic design methods were introduced in literature as simple suboptimal solutions of DPA. Therefore, DPA strategic design methods and FPA techniques are simple approaches to implement PD-NOMA in 6G and beyond. In literature, no previous work had compared the performances of all DPA strategic design methods, nor FPA techniques, to ease the selection of a simple strategy for PD-NOMA in 6G. Motivated by that, this work compares performances of all DPA strategic design methods as well as FPA techniques, in terms of sum-rate capacity, fairness, and bit error rate (BER). Results showed that the best DPA strategic design method and the best FPA technique have comparable performance.

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“…In [8], a gain ratio power allocation (GRPA) strategy that considered users' channel conditions to ensure efficient and fair power allocation was proposed, whereas in [12] optimal power allocation schemes for both static and mobile users were proposed. In [13], a user grouping and power allocation method was proposed for NOMA-VLC multi-cell networks and in [14] a downlink power allocation scheme was proposed to achieve a flexible tradeoff between the sum rate and user fairness. The experimental demonstration of NOMA-VLC links with higher system capacity were reported in [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Based Signal Demodulator in NOMA-VLC

Lin

Lai

Ghassemlooy

et al. 2021

J. Lightwave Technol.

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Non-orthogonal multiple access (NOMA) is a promising scheme to improve the spectral efficiency, user fairness, and overall throughput in visible light communication (VLC) systems. However, the error propagation problem together with linear and nonlinear distortions induced by multipath, limited modulation bandwidth, and nonlinearity of light emitting diode significantly limit the transmission performance of NOMA-VLC systems. In addition, having an accurate channel state information, which is important in the recovery of the NOMA signal, in mobile wireless VLC is challenging. In this work, we propose a convolutional neural network (CNN)-based demodulator for NOMA-VLC, in which signal compensation and recovery are jointly realized. Both simulation and experiment results show that, the proposed CNN-based demodulator can effectively compensate for both linear and nonlinear distortions, thus achieving improved bit error ratio performance compared with the successive interference cancellation and joint detection-based receivers. Compared to SIC, the performance gains are 1.9, 2.7, and 2.7 dB for User1 for the power allocation ratios (PARs) of 0.16, 0.25, and 0.36, respectively, which are 4, 4, and 2.6 dB for User2 for PARs of 0.16, 0.25, and 0.36, respectively. Index Terms-visible light communications (VLC), nonorthogonal multiple access (NOMA), convolutional neural network (CNN).

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Differential evolution-based optimal power allocation scheme for NOMA-VLC systems

Cited by 20 publications

References 15 publications

A Survey of NOMA for VLC Systems: Research Challenges and Future Trends

A Survey of NOMA for VLC Systems: Research Challenges and Future Trends

On The Selection of Power Allocation Strategy in Power Domain Non‐Orthogonal Multiple Access (PD‐NOMA) for 6G and Beyond

A Machine Learning Based Signal Demodulator in NOMA-VLC

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