Energy Harvesting From Multiple RF Sources in Wireless Fading Channels

Altınel, Doğay; Kurt, Güneş Karabulut

doi:10.1109/tvt.2016.2515664

Cited by 40 publications

(4 citation statements)

References 23 publications

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“…Three chip system contains emulation resistor circuit and radio frequency DC rectifier [9]. The some assumption are taken for wireless systems in response of energy harvesting those having a constant conversion of energy from energy harvesters [10], also energy harvesting technology is under deployment phase to facilitate low power devices in wireless networks [11]. To analyze energy harvesting based cognitive radio system.…”

Section: Literature Studymentioning

confidence: 99%

Analysis and Maximizing Energy Harvesting from RF Signals using T-Shaped Microstrip Patch Antenna

Iqbal¹,

Khanzada²,

Dahri³

et al. 2019

ijacsa

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The advancement of the modern world requires catering the power crisis. New methodologies for energy harvesting were considered, but their succession in a different environment is still to explore. This paper deals with antenna designing to harvest energy from radio signals. The rummage of energy from surrounding sources is considered a harvesting of energy and it would be an alternative approach for low energy utilization. As comparatively well-known sources are considered for energy harvesting; such as wind and solar, radio frequency signal can provide continues supply of energy harvesting. Alternatively, we are getting the maximum usable energy resources which are challenging the amplitude of arriving signal, which is considered very low and the requirements for operating available antennas are proportionally higher. Using the Microstrip patch antenna limited the energy resources, because it is low profile, easy to configure, simple in design at the lowest rate. Furthermore, the combined the configuration and proposed antenna design provide the maximum energy efficiency. More simulation iterations are performed to maximize the gain of ISM band of 2.4 GHz. The operating frequency of microstrip patch antenna is 2.4GHz, which provides the gain of 7.2dB, return loss-20dB and the directivity of 7.44dB. The achieved result of source voltage is 900 mv after rectification the output voltage 2.5v. The results are efficient and suitable to overcome litter bit power crisis.

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Section: Literature Studymentioning

confidence: 99%

Analysis and Maximizing Energy Harvesting from RF Signals using T-Shaped Microstrip Patch Antenna

Iqbal¹,

Khanzada²,

Dahri³

et al. 2019

ijacsa

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“…Modeling by the direct use of gamma distribution fit via parameter estimation [22,23,24]. Approximating a phenomenon, for example, signal and interference powers [25,26], co-channel interference [27], rendezvous time [28], simulation results in fading wireless channel conditions [29], action duration and inter-arrival [30] and resource requirements for traffic characteristics [31], and Derivation of closed forms of models that follows gamma distribution. The closed forms are useful in modelling wireless network systems and other related models as seen in beamforming [32], bit error rate [33,34] and average bit error rate [35].…”

Section: Introductionmentioning

confidence: 99%

“…Approximating a phenomenon, for example, signal and interference powers [ 25 , 26 ], co-channel interference [ 27 ], rendezvous time [ 28 ], simulation results in fading wireless channel conditions [ 29 ], action duration and inter-arrival [ 30 ] and resource requirements for traffic characteristics [ 31 ], and…”

Section: Introductionmentioning

confidence: 99%

Approximations for the inverse cumulative distribution function of the gamma distribution used in wireless communication

Okagbue

Adamu

Anake

2020

Heliyon

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The use of quantile functions of probability distributions whose cumulative distribution is intractable is often limited in Monte Carlo simulation, modeling, and random number generation. Gamma distribution is one of such distributions, and that has placed limitations on the use of gamma distribution in modeling fading channels and systems described by the gamma distribution. This is due to the inability to find a suitable closed-form expression for the inverse cumulative distribution function, commonly known as the quantile function (QF). This paper adopted the Quantile mechanics approach to transform the probability density function of the gamma distribution to second-order nonlinear ordinary differential equations (ODEs) whose solution leads to quantile approximation. Closed-form expressions, although complex of the QF, were obtained from the solution of the ODEs for degrees of freedom from one to five. The cases where the degree of freedom is not an integer were obtained, which yielded values closed to the R software values via Monte Carlo simulation. This paper provides an alternative for simulating gamma random variables when the degree of freedom is not an integer. The results obtained are fast, computationally efficient and compare favorably with the machine (R software) values using absolute error and Kullback-Leibler divergence as performance metrics.

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“…This randomness will affect the amount of the harvested energy. Such randomness was investigated in [11] in terms of battery recharging time, but with limited discussion. Secondly, the received energy will only become usable when it is converted by an energy harvester.…”

Section: Introductionmentioning

confidence: 99%