Low-Complexity PSO-Based Resource Allocation Scheme for Cooperative Non-Linear SWIPT-Enabled NOMA

Garcia, Carla E.; Camana, Mario R.

doi:10.1109/access.2022.3162838

Cited by 25 publications

(15 citation statements)

References 37 publications

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“…PSO begins by reading the main parameters, such as the W, C 1 , C 2 , i , and swarm values. The value of W was set to 0.7, and C 1 and C 2 were set to 1.494, as previously recommended [58, 59]. The iteration i was set to 100, and 10 swarm values between 5 and 50 were explored in five swarm increments.…”

Section: Methodsmentioning

confidence: 99%

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Hybridization of particle swarm optimization algorithm with neural network for COVID‐19 using computerized tomography scan and clinical parameters

Sameer

Gharghan

Mutlag

2023

The Journal of Engineering

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The 2019 coronavirus disease began in Wuhan, China, and spread worldwide. This pandemic was concerning, given its significant and worrying impact on human health. Strategies to manage the disease begin with diagnosing the infection, often using the real-time reverse transcription polymerase chain reaction (RT-PCR) assay. However, this process is time intensive. Therefore, alternative rapid methods to diagnose the coronavirus with high accuracy are needed. X-ray and computerized tomography (CT) scans are reasonable solutions for rapid coronavirus diagnosis. The dataset of 500 patients was tested, including 286 uninfected patients and 214 infected with COVID-19. Clinical parameters, including heart rate (HR), temperature (T), blood oxygen level, D-dimer, and CT scan, including red-green-blue (RGB) pixel values of the left and right lungs, were collected from 500 patients and used to train an artificial neural network (ANN) to diagnose coronavirus. The ANN was hybridized with a particle swarm optimization (PSO) algorithm to improve diagnosis accuracy. The results show that the proposed PSO-ANN method significantly improved diagnosis accuracy (98.93%), sensitivity (100%), and specificity (98.13%). The effectiveness of the proposed method was confirmed by comparing the findings with those of previous studies.

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Section: Methodsmentioning

confidence: 99%

“…and C 1 and C 2 were set to 1.494, as previously recommended [58,59]. The iteration i was set to 100, and 10 swarm values between 5 and 50 were explored in five swarm increments.…”

Section: Pso Begins By Reading the Main Parameters Such As The Wmentioning

confidence: 99%

Hybridization of particle swarm optimization algorithm with neural network for COVID‐19 using computerized tomography scan and clinical parameters

Sameer

Gharghan

Mutlag

2023

The Journal of Engineering

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“…-Particle Swarm Optimization (PSO): It is an evolutionary algorithm based on concepts such as social behavior and swarm intelligence principles [22,23,39]. In each generation, the velocity and position of each particle are updated following their equations [40,41].…”

Section: Heuristic Optimization Applied To Power Systemsmentioning

confidence: 99%

Optimum frequency response with decentralized drivers in power system

Maldonado

Cepeda

Gamboa

et al. 2023

IET Generation Trans & Dist

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This document presents a novel methodology to optimize the power system frequency response based on the implementation of decentralized controllers. The proposed method focuses on the control of the internal rotor angle in the selected generator, which allows its governor to act as a decentralized controller that requires as input local measurements. Through a proposed optimization process, it is first possible to establish a set of optimal parameters for each controller and, second, to determine the required phasor measurement units (PMUs) and their locations that are necessary to be installed in a power system. This comprehensive proposal is based on the time domain evaluation of the frequency response when multiple scenarios of deviations between demand and generation occur. Finally, the new proposal is applied to the IEEE 39-bus test system to validate its advantages; for this, the test power system as well as the proposed controllers are implemented in DIgSILENT PowerFactory, along with Python to manage the optimization process.

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“…In addition to performing information decoding, the l th EH user will perform energy harvesting by utilizing the other part of the received signal, i.e., √ 1 − α l y EH l . By adopting the non-linear harvesting model, the amount of the harvested energy at the harvester module is expressed as follows [10]…”

Section: System Modelmentioning

confidence: 99%

“…On the other hand, the optimization of SWIPT-based systems for transmission has been studied in various works. Examples include joint transmit beamforming and receive power splitting design for a multiuser MISO broadcast system in [9], SWIPTenabled NOMA systems in [10], and the integration of RSMA with SWIPT-based systems using RIS in [11]. These works aimed to improve the efficiency and power constraints of SWIPT-based systems.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for RSMA-Based Multi-Functional Wireless Networks

Naser,

Ali,

Muhaidat

2023

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

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The upcoming sixth generation (6G) is expected to support a wide range of applications that require efficient sensing, accurate localization, and reliable communication capabilities. Furthermore, 6G is expected to catalyze the development of new use cases that will require working in extreme environmental and hazardous conditions and have ultra-small size and low-cost wireless devices. Thus, developing sustainable multi-functional wireless networks that are capable of incorporating billions of low-power devices and supporting their sensing and communication requirements on top of energy harvesting capability is of paramount importance. Motivated by this, we consider in this work a ratesplitting multiple access (RSMA)-based multifunctional wireless network with sensing, energy harvesting, and communication capabilities. We employ trust region policy optimization (TRPO), a deep reinforcement learning (DRL) algorithm, to efficiently allocate the available resources and manage the interference between the three functionalities. TRPO/DRL is capable to learn a near-optimal policy for the resource allocation problem in a complex and dynamic environment. This enables us to obtain near-optimal transmit precoders, power splitting ratios, and ratesplitting among the common and private rates in a multiple access setting. Simulation results demonstrate the effectiveness of RSMA in mitigating the interference in such multi-functional networks and its capability to accommodate the rate and energy harvesting requirements of the devices while still capable of sensing multiple targets.

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Low-Complexity PSO-Based Resource Allocation Scheme for Cooperative Non-Linear SWIPT-Enabled NOMA

Cited by 25 publications

References 37 publications

Hybridization of particle swarm optimization algorithm with neural network for COVID‐19 using computerized tomography scan and clinical parameters

Hybridization of particle swarm optimization algorithm with neural network for COVID‐19 using computerized tomography scan and clinical parameters

Optimum frequency response with decentralized drivers in power system

Deep Reinforcement Learning for RSMA-Based Multi-Functional Wireless Networks

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