A CLSTM and transfer learning based CFDAMA strategy in satellite communication networks

He, Qiang; Xiang, Zheng; Ren, Pinyi

doi:10.1371/journal.pone.0248271

Cited by 3 publications

(3 citation statements)

References 19 publications

(19 reference statements)

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“…It has advantages for demand, free assignment, and better performance in satellite communication networks. 79 CFDAMA protocol has a time delay in sending a request to a satellite and receiving a reply. Using that, a proposed system that works on Transfer learning with CLSTM on CFDAMA is proposed for a satellite onboard prediction time prediction strategy.…”

Section: Protocolsmentioning

confidence: 99%

“…These experiments portrayed that the proposed methodology outperformed conventional CFDAMA-PA and CFDAMA-PB protocols. 79 Satellite communications are used for purposes like high-speed broadband and other specialized applications. It has a wide coverage area and is flexible.…”

Section: Protocolsmentioning

confidence: 99%

“…It assigns slots in a frame on‐demand only basis. It has advantages for demand, free assignment, and better performance in satellite communication networks 79 …”

Section: Satellite Communication System Characteristicsmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning and Deep Learning powered satellite communications: Enabling technologies, applications, open challenges, and future research directions

Bhattacharyya

Nambiar

Ojha

et al. 2023

Satell Commun Network

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SummaryThe recent wave of creating an interconnected world through satellites has renewed interest in satellite communications. Private and government‐funded space agencies are making advancements in the creation of satellite constellations, and the introduction of 5G has brought a new focus to a fully connected world. Satellites are the proposed solutions for establishing high throughput and low latency links to remote, hard‐to‐reach areas. This has caused the injection of many satellites in Earth's orbit, which has caused many discrepancies. There is a need to establish highly adaptive and flexible satellite systems to overcome this. Machine Learning (ML) and Deep Learning (DL) have gained much popularity when it comes to communication systems. This review extensively provides insight into ML and DL's utilization in satellite communications. This review covers how satellite communication subsystems and other satellite system applications can be implemented through Artificial Intelligence (AI) and the ongoing open challenges and future directions.

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Section: Protocolsmentioning

confidence: 99%

Section: Protocolsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning and Deep Learning powered satellite communications: Enabling technologies, applications, open challenges, and future research directions

Bhattacharyya

Nambiar

Ojha

et al. 2023

Satell Commun Network

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Transfer Learning Approach for 6G-IoT Applications

Upadhyaya

Ruchi

Dutt

2022

2022 7th International Conference on Communication and Electronics Systems (ICCES)

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Adaptive Access Selection Algorithm for Large-Scale Satellite Networks Based on Dynamic Domain

Liu

Jiang

et al. 2022

Sensors

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The traditional satellite access selection algorithm, which is used in large-scale satellite networks, has some disadvantages, such as frequent link switching, high interrupt probability, and unable to adapt to a dynamic environment. According to the periodicity of the large-scale satellite network and the prior knowledge provided by acknowledgment packages, a dynamic domain-based adaptive access algorithm (DAA) is proposed in this paper. Firstly, this algorithm divides the large-scale satellite network into different domains according to the minimum elevation angle of the Earth station (ES) and the predictable characteristics of the trajectory of the satellite. Then, the ES selects the access satellites according to the relationship between the traffic volume and the satellites’ coverage time. Finally, the ES selects the backup access satellite based on the satellites’ coverage time, the traffic volume of the ES, satellite status provided by prior knowledge, and other information. When the access satellite cannot satisfy the communication demand, the ES adaptively switches the earth-satellite link to the backup access satellite. The ES first choice of access satellite does not require interaction with the satellites, reducing the consumption of communication resources. The selection strategy of backup access satellite and the concept of virtual destination address proposed in this paper can reduce the routing overhead after switching. Through theoretical analysis and simulation results in the StarLink constellation, it is proved that this paper improves the coverage time utilization of accessing satellites and reduces the switching probability compared with the traditional access algorithm, which is more suitable for ES to access large-scale satellite networks.

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A CLSTM and transfer learning based CFDAMA strategy in satellite communication networks

Cited by 3 publications

References 19 publications

Machine Learning and Deep Learning powered satellite communications: Enabling technologies, applications, open challenges, and future research directions

Machine Learning and Deep Learning powered satellite communications: Enabling technologies, applications, open challenges, and future research directions

Transfer Learning Approach for 6G-IoT Applications

Adaptive Access Selection Algorithm for Large-Scale Satellite Networks Based on Dynamic Domain

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