Performance of Opportunistic Receiver Beam Selection in Multiaperture OWC Systems Over Foggy Channels

Rahman, Ziyaur; Zafaruddin, S. M.; Chaubey, V. K.

doi:10.1109/jsyst.2019.2962580

Cited by 23 publications

(10 citation statements)

References 46 publications

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“…The Haze and fog particle consists of a mixture of very fine water droplets or ice near the surface of the Earth [113]. The light is scattered by these particles and hence reduces visibility.…”

Section: C) Fogmentioning

confidence: 99%

“…The light is scattered by these particles and hence reduces visibility. Fog is characterized as less than 1 km of visibility and relative humidity that reaches the level of saturation (100 %) [113], [114]. Some parameters, such as distribution of particle size, liquid water content (LWC), temperature and humidity, etc.…”

Section: C) Fogmentioning

confidence: 99%

“…In fact, the origin of fog attenuation models using visibility data stems from the atmospheric visibility concept itself [116]. Visibility is characterised as the distance from an object where the distinction between images drops to a certain percentage of what it would be if the object were instead nearby [113]. In visibility description, image separation that drops to 2 percent and 5 percent is considered.…”

Section: C) Fogmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Free Space Optics (FSO) Communication Systems, Links, and Networks

et al. 2021

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Section: C) Fogmentioning

confidence: 99%

Section: C) Fogmentioning

confidence: 99%

Section: C) Fogmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Free Space Optics (FSO) Communication Systems, Links, and Networks

et al. 2021

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“…In appreciation of the current learning models availed in different machine learning-based problem analysis, there is existing models of single and 2D only. Based on the most recurrent studies of beam selection for instance, various effective algorithms to find the active beam set and the user power allocation have been proposed in search of efficiency in communication, using the DL-centered mmWave beam selection for fifth-generation applicability of NR and 6Gbased access to an unlicensed spectrum with sub-6 gigahertz channel information for proper algorithmic and prototypic validations, propagation of the high speed transport category of the railway searching for the lower complexity of beam selection, increased performance and evaluation of adaptable receiver beam selections, intersecting index-based joint beam selection for mmWave multiuser multiple-input and multiple-output systems, and simplified spatial data mining and beam selection [13][14][15][16][17][18]. This paper is proposing an alternative way to attack the problem to network issue analysis to the acknowledged medical network situations based on the prevailing readings, prototypes, and designs, which are clarified on the medical application illustrated above in 3D.…”

Section: Motivationmentioning

confidence: 99%

A 5G Beam Selection Machine Learning Algorithm for Unmanned Aerial Vehicle Applications

Shafik

Matinkhah

Ahmad

2020

Wireless Communications and Mobile Computing

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The unmanned aerial vehicles (UAVs) emerged into a promising research trend within the recurrent year where current and future networks are to use enhanced connectivity in these digital immigrations in different fields like medical, communication, and search and rescue operations among others. The current technologies are using fixed base stations to operate onsite and off-site in the fixed position with its associated problems like poor connectivity. This open gate for the UAV technology is to be used as a mobile alternative to increase accessibility with fifth-generation (5G) connectivity that focuses on increased availability and connectivity. There has been less usage of wireless technologies in the medical field. This paper first presents a study on deep learning to medical field application in general and provides detailed steps that are involved in the multiarmed bandit (MAB) approach in solving the UAV biomedical engineering technology device and medical exploration to exploitation dilemma. The paper further presents a detailed description of the bandit network applicability to achieve close optimal performance and efficiency of medical engineered devices. The simulated results depicted that a multiarmed bandit problem approach can be applied in optimizing the performance of any medical networked device issue compared to the Thompson sampling, Bayesian algorithm, and ε-greedy algorithm. The results obtained further illustrated the optimized utilization of biomedical engineering technology systems achieving thus close optimal performance on the average period through deep learning of realistic medical situations.

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“…To minimize the effects of atmospheric turbulence-induced fading and pointing errors, spatial diversity techniques were studied in [5]- [8]. The FSO communication systems with multiple aperture receivers were investigated in [5] and [6] by employing selection combining (SC) and maximal-ratio combining (MRC) techniques, respectively. In [7], bit error rate (BER) analysis of a multiple-input multiple-output (MIMO) FSO communication system with equal gain combining (EGC) and MRC was carried out.…”

Section: Introductionmentioning

confidence: 99%

On the Capacity Performance of Hybrid FSO/RF System With Adaptive Combining Over Generalized Distributions

Vishwakarma

Swaminathan

2022

IEEE Photonics J.

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This paper investigates the ergodic capacity performance of hybrid free space optics (FSO)/ radio frequency (RF) system by employing an adaptive-combining-based switching scheme. In the adaptive combining scheme, the FSO and RF links are combined using the maximal-ratio combining (MRC) at the output, when the instantaneous signal-to-noise ratio (SNR) of the FSO link drops below a predefined threshold SNR value. The FSO link experiences the atmospheric turbulence, which is modeled using the generalized Malaga distribution, non-zero boresight misalignment or pointing errors, and atmospheric attenuation, under two types of detection techniques, i.e. heterodyne detection (HD) and intensity modulation/direct detection (IM/DD). Similarly, the fading of the RF link is characterized using the generalized κ-µ distribution. In particular, we derive the probability density function (PDF) of the instantaneous SNR of the adaptive combining scheme. Capitalizing on the SNR statistics, we obtain the unified closed-form ergodic capacity expressions for the adaptive-combining-based hybrid FSO/RF system. In addition, the asymptotic expressions for the ergodic capacity of the system at the high-SNR region are presented. All the derived analytical expressions are validated by using Monte-Carlo simulations. Numerical results and discussions are provided to compare the ergodic capacity performance of the adaptive-combining-based hybrid FSO/RF system with the existing system models such as single-link FSO, MRC-based hybrid FSO/RF, and hard-switching-based hybrid FSO/RF systems.

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Performance of Opportunistic Receiver Beam Selection in Multiaperture OWC Systems Over Foggy Channels

Cited by 23 publications

References 46 publications

A Survey of Free Space Optics (FSO) Communication Systems, Links, and Networks

A Survey of Free Space Optics (FSO) Communication Systems, Links, and Networks

A 5G Beam Selection Machine Learning Algorithm for Unmanned Aerial Vehicle Applications

On the Capacity Performance of Hybrid FSO/RF System With Adaptive Combining Over Generalized Distributions

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