Enhancing Resilience of FSO Networks to Adverse Weather Conditions

Kalesnikau, Ilya; Pióro, Michał; Rak, Jacek; Ivanov, Hristo; Fitzgerald, Emma; Leitgeb, Erich

doi:10.1109/access.2021.3107243

Cited by 7 publications

(4 citation statements)

References 63 publications

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“…FSO’s main advantages are its ability to provide low power consumption, high capacity, low price, and license-free option, and it can be used to set up point-to-multipoint links. The performance of FSO is often affected by visibility and adverse weather conditions such as fog, snowfall, or rain, making it unsuitable for long distances or non-line of sight (NLoS) links [ 27 , 28 , 29 ].…”

Section: Xran Architecture Developments and Fronthaul Enabling Techno...mentioning

confidence: 99%

Design of Cost-Efficient Optical Fronthaul for 5G/6G Networks: An Optimization Perspective

Fayad

Cinkler

Rak

et al. 2022

Sensors

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Currently, 5G and the forthcoming 6G mobile communication systems are the most promising cellular generations expected to beat the growing hunger for bandwidth and enable the fully connected world presented by the Internet of Everything (IoE). The cloud radio access network (CRAN) has been proposed as a promising architecture for meeting the needs and goals of 5G/6G (5G and beyond) networks. Nevertheless, the provisioning of cost-efficient connections between a large number of remote radio heads (RRHs) in the cell sites and the baseband unit (BBU) pool in the central location, known as the fronthaul, has emerged as a new challenge. Many wired and wireless solutions have been proposed to address this bottleneck. Specifically, optical technologies presented by passive optical networks (PONs) are introduced as the best suitable solution for 5G and beyond network fronthaul due to their properties of providing high capacity and low latency connections. We considered time and wavelength division multiplexed passive optical networks (TWDM-PONs) as a fronthaul for 5G and beyond. Taking that into consideration, in this paper, we propose an integer linear program (ILP) that results in the optimal optical fronthaul deployment while minimizing the total cost of 5G and beyond instances. However, for larger network instances, solving the ILP problem becomes unscalable and time-consuming. To address that, we developed two heuristic-based algorithms (the K-means clustering algorithm and the one based on the genetic algorithm—GA). We evaluated the suitability of our proposed ILP and heuristic algorithms in simulations by utilizing them to plan different network instances (dense and sparse).

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Section: Xran Architecture Developments and Fronthaul Enabling Techno...mentioning

confidence: 99%

Design of Cost-Efficient Optical Fronthaul for 5G/6G Networks: An Optimization Perspective

Fayad

Cinkler

Rak

et al. 2022

Sensors

Self Cite

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“…The decision tree algorithm method is used to solve regression and classification problems [13]. The result of using a decision tree is an educational model that can be used to predict the value or class of the resulting variable using the way individual learning rules derived from the training data are learned [14].…”

Section: Machine Learningmentioning

confidence: 99%

Investigation of Machine Learning Methods for Prediction of Measured Values of Atmospheric Channel for Hybrid FSO/RF System

et al. 2022

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This research paper addresses the problems of fiberless optical communication, known as free space optics, in predicting RSSI (Received Signal Strength Indicator) parameters necessary for hard switching in a hybrid FSO/RF (Free Space Optics/Radio Frequency) system. This parameter is used to determine the intensity of the transmitted signal (in our case, a light beam) from one FSO head to another. Since we want to achieve almost 100% reliability, it is important to know the parameters of the transmission environment for the FSO and RF lines. Each of them has its limitations and, as a result, a weather monitoring station is required. The FSO is mostly affected by fog and the concentration of particles in the air, while the RF line is affected by rain and snow. It is precisely due to these influences that it is necessary (based on the mentioned RSSI parameter) to switch using the hard switching method from the primary FSO line to the backup RF line by correctly predicting this value. If the value of the RSSI parameter falls below the critical level—42 dBm—the system automatically switches to the backup RF line. There are several ways we can predict this parameter. One of them is machine learning methods such as decision trees. Our research focused on the prediction of the RSSI parameter, the methods of decision trees and decision trees using the AdaBoost regressor. Since we want to correctly predict the RSSI parameter, it is also necessary to choose the right way to predict it based on the recorded weather conditions. If we want to correctly use the hard switching method in hybrid FSO/RF systems, it is necessary to choose the correct method of predicting the RSSI parameter, which serves as an indicator for switching from the primary FSO line to the secondary RF line. Therefore, we decided to investigate methods of machine learning—the decision tree and the decision tree with the use of an AdaBoost Regressor. The main benefit of this paper is the improvement of existing machine learning methods (decision trees and decision trees using the AdaBoost regressor) for the correct prediction of the RSSI parameter for the needs of hard switching in a hybrid FSO/RF system. The method chosen in this manuscript has very good results. As can be seen in the attached graphs, over a longer period and using correctly selected training data, it is possible to achieve ideal results for the prediction of the RSSI parameter. The tables also show the effectiveness of the prediction, and the fact that it is best to train on either the first- or third-minute data. In the future, it would be appropriate to implement weather prediction or to consider other methods, such as random forests or neural networks.

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“…Degradation of channel capacity can be reduced by using mid -IR optical signals [16]. Fog link attenuation models can be developed based on Kim and Kruse distribution [18]. OWC system performance is affected by atmospheric losses.…”

Section: Introductionmentioning

confidence: 99%

Laser based Optical Wireless Communication using Log Normal Distribution and SIM BPSK Modulation

Belgaonkar,

Sundaraguru

2023

IJRITCC

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Laser based Optical Wireless Communication(OWC) is emerging as one of the key advancements for the 5G and beyond technologies. This technology provides high bandwidth, extremely high data rate and high speed communication due to the use of optical waves for data transmission in the field of wireless communication. The system performance is limited due to the presence of turbulence in the atmosphere.We have developed system model using log normal channel distribution in the weak to moderate atmospheric turbulence. Closed form of expression is derived which provides the channel capacity in the presence of weak turbulence using log normal distribution. Subcarrier Internsity Modulation (SIM) BPSK modulation technique is used in OWC system which provides bettter performance in terms of received BER as compared to NRZ – OOK modulation.

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Enhancing Resilience of FSO Networks to Adverse Weather Conditions

Cited by 7 publications

References 63 publications

Design of Cost-Efficient Optical Fronthaul for 5G/6G Networks: An Optimization Perspective

Design of Cost-Efficient Optical Fronthaul for 5G/6G Networks: An Optimization Perspective

Investigation of Machine Learning Methods for Prediction of Measured Values of Atmospheric Channel for Hybrid FSO/RF System

Laser based Optical Wireless Communication using Log Normal Distribution and SIM BPSK Modulation

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