On the Design of RIS-UAV Relay-Assisted Hybrid FSO/RF Satellite-Aerial-Ground Integrated Network

Nguyen, Thang V.; Le, Hoang D.; Pham, Anh T.

doi:10.1109/taes.2022.3189334

Cited by 51 publications

(20 citation statements)

References 33 publications

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“…The effect of the atmospheric turbulence distributing the optical signal link gives rise to optical beam deviations. Such optical beam deviation is caused by the atmospheric turbulence brought on by phase shifts of the transmitted optical beams [34], [35], [36]. Various weather scenarios ended up with scintillation, which distorts the optical link significantly.…”

Section: B Atmospheric Turbulence In Fsomentioning

confidence: 99%

“…But the closed-form expressions were not derived for such a system. Similarly, in [36], authors improve the optical system's performance by considering the Log-normal and Gamma-Gamma distributions with the assumptions of pointing error, beam width, pointing error variance, and detector size. Some methods in [37], [38], [39], and [40] consider the intensity modulation/direct direction (IM/DD), the misalignment paradigm, and the closed-form expression of the BER assuming moderate scintillation and pointing error was derived [37], [41], [42], [43].…”

Section: Mitigation Techniquesmentioning

confidence: 99%

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Error Analysis of Free Space Communication System Using Machine Learning

2023

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Free space optical (FSO) communication offers huge bandwidth, license-free spectrum and a more secure channel. PIN diodes are normally used for detection, but avalanche photodiodes (APD) are preferred for detecting high-speed FSO signals in many applications. In the case of APD, the noise distribution is input-dependent Gaussian noise (IDGN) rather than input-independent Gaussian noise (IIGN). We investigate the error analysis using on-off keying (OOK) for various detection approaches. This paper proposes a machine learning approach and compares its performance with soft and hard decisions. Soft values in the case of IDGN and IIGN are derived, and the optimum and sub-optimum detection thresholds are evaluated. The proposed novel ML approach shows better performance gains than the other approaches. It is also demonstrated that the IDGN model should have an optimum detection and achieve a gain of 2.5[dB] and about 1[dB] at λ = 0[dB] and λ = 10[dB], respectively. Experimental results are plotted for the FSO channel data, and a model fit curve is plotted using the ML approach.INDEX TERMS Error analysis, machine learning, optical communications, hard decision, soft decision.

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Section: B Atmospheric Turbulence In Fsomentioning

confidence: 99%

Section: Mitigation Techniquesmentioning

confidence: 99%

Error Analysis of Free Space Communication System Using Machine Learning

2023

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“…In Ref. 91, for encountering the drawbacks of hybrid RF-FSO systems, i.e., when FSO link is blocked due to cloudy weather and inherent nature of RF links to offer comparatively low bandwidth, a new model involving HAP-assisted satellite aerial ground integrated network and RIS based UAV relay, is presented. Three strategies have been used for carrying out the communication between satellite, HAP, and ground station.…”

Section: Latest Contributions In Uav-based Fso Networkmentioning

confidence: 99%

Review on UAV-based FSO links: recent advances, challenges, and performance metrics

Gupta,

Dhawan,

Gupta

2023

Opt. Eng.

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With the rapid advancements in wireless technology, the incorporation of unmanned aerial vehicles (UAVs) in free space optical (FSO) communication can reap several benefits related to coverage, security, and capacity. The parameters involved for the analysis of such systems are studied in detail. The irradiance fluctuations in the received beam due to turbulence induced fading and geometric and misalignment effects are to be taken care of in order to minimize the bit error rate. The random variables involved in a UAV-employed FSO link are larger than that present in an FSO system. Thus, efficient designing of a UAV-employed FSO system is relatively more challenging as compared to a ground-based terrestrial FSO link. There are many performance metrics that can be defined and are to be analyzed in order to optimize the parameters associated with UAV-based FSO systems and design a link with good quality of service. Some recent methods are also explored for further improving the reliability and coverage of UAV based FSO networks.

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“…The communication delay between satellites is shown in Table 3. UAVs are characterized by versatility and high mobility and can provide communication services through a communication transceiver employed as an aerial communication platform [21,22]. UAVs can also be used as aerial hosts to realize various applications from cargo delivery to surveillance [23,24].…”

Section: Experimental Settingmentioning

confidence: 99%

DRL-G: SFC Deployment Algorithm Based on Deep Reinforcement Learning in Space–Air–Ground Integrated Network

Feng,

He,

Zhuang

et al. 2023

Preprint

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The space-air-ground integrated network (SAGIN) is formed by the fusion of ground and aircraft networks. It overcomes the limitations in communications that cannot cover the whole world, bringing new opportunities for network communication in remote areas. However, the existence of many heterogeneous devices in SAGIN poses significant challenges in terms of end-to-end resource management, and the limited regional heterogeneous resources also affect the quality of service (QoS) for users. In this context, this study proposes a hierarchical resource management structure for SAGIN, named SAGIN-MEC, based on a software-defined network (SDN), network function virtualization (NFV), and mobile edge computing (MEC), aiming to facilitate the systematic management of heterogeneous network resources. Furthermore, to minimize operator deployment costs while ensuring QoS, this study formulates a resource scheduling optimization model tailored for SAGIN scenarios to minimize energy consumption. Additionally, we propose a deployment algorithm named DRL-G, which is based on heuristics and deep reinforcement learning (DRL), aiming to allocate heterogeneous network resources within SAGIN effectively. Experimental results show that SAGIN-MEC can reduce the end-to-end delay by 6-15 ms compared to the terrestrial edge network. In addition, compared to other algorithms, DRL-G can improve the service request reception rate by up to 28%.

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On the Design of RIS-UAV Relay-Assisted Hybrid FSO/RF Satellite-Aerial-Ground Integrated Network

Cited by 51 publications

References 33 publications

Error Analysis of Free Space Communication System Using Machine Learning

Error Analysis of Free Space Communication System Using Machine Learning

Review on UAV-based FSO links: recent advances, challenges, and performance metrics

DRL-G: SFC Deployment Algorithm Based on Deep Reinforcement Learning in Space–Air–Ground Integrated Network

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