XiA: Send-It-Anyway Q-Routing for 6G-Enabled UAV-LEO Communications

Deb, Pallav Kumar; Mukherjee, Anandarup; Misra, Sudip

doi:10.1109/tnse.2021.3086484

Cited by 18 publications

(7 citation statements)

References 21 publications

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“…Although effective in reducing energy consumption and extending network lifetime, FAN-RL only considers the impact of network density and stability on the time the network provides service, the amount of UAV power, and the number of failed links, and does not consider other factors such as link jitter. Kumar et al [99] proposed an algorithm for delay-sensitive FANETs called XiA that utilizes Q-learning and considers the SAGIN's FANET characteristics, designing a bonus function that factors in channel conditions and equipment configurations to minimize the time required to forward data to the access point via multiple relay UAVs, including those in restricted or affected communication areas. If the UAVs are unable to reach the access point, they send the data to the LEO satellite, which then forwards the data to the access point.…”

Section: Reinforcement-learning-based Routing Algorithmsmentioning

confidence: 99%

UAV Ad Hoc Network Routing Algorithms in Space–Air–Ground Integrated Networks: Challenges and Directions

Zhang

Igorevich

et al. 2023

Drones

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With the rapid development of 5G and 6G communications in recent years, there has been significant interest in space–air–ground integrated networks (SAGINs), which aim to achieve seamless all-area, all-time coverage. As a key component of SAGINs, flying ad hoc networks (FANETs) have been widely used in the agriculture and transportation sectors in recent years. Reliable communication in SAGINs requires efficient routing algorithms to support them. In this study, we analyze the unique communication architecture of FANETs in SAGINs. At the same time, existing routing protocols are presented and clustered. In addition, we review the latest research advances in routing algorithms over the last five years. Finally, we clarify the future research trends of FANET routing algorithms in SAGINs by discussing the algorithms and comparing the routing experiments with the characteristics of unmanned aerial vehicles.

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Section: Reinforcement-learning-based Routing Algorithmsmentioning

confidence: 99%

UAV Ad Hoc Network Routing Algorithms in Space–Air–Ground Integrated Networks: Challenges and Directions

Zhang

Igorevich

et al. 2023

Drones

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“…There are various previous studies on selecting the best communication destination in the NTN [15] and deploying the ground stations to avoid cloud effects [5][6][7][8][9]. To increase transmission speed and reduce processing delay in Satellite-Terrestrial Networks (STN), an application of Multi-access Edge Computing (MEC) to STN has been also studied [12][13][14].…”

Section: Related Workmentioning

confidence: 99%

“…There are some previous studies that deal with the issue of signal attenuation due to atmospheric channels in satellite-terrestrial optical satellite communications. In [15], atmospheric channels in optical communications were modeled, and a routing algorithm based on Q-learning has been proposed for communication between a group of unmanned aerial vehicles (UAVs) and low earth orbit (LEO). Those previous studies have considered satellite-terrestrial optical communications when only a single optical ground station is available.…”

Section: Related Workmentioning

confidence: 99%

“…In the system models of those previous works, satellite-terrestrial optical communications are interrupted by the cloud; therefore, the ground stations have to be appropriately deployed for high network availability. Reference [15] has targeted a model that UAVs selects which to transmit to in an environment where the channel conditions change.…”

Section: System Modelmentioning

confidence: 99%

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Applying reinforcement learning algorithms to ground station selection in satellite-terrestrial optical communication

Makizoe

Yumoto

Oshima

et al. 2023

NOLTA

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Non-terrestrial networks, composed of ground, air, and satellite communications, are considered one of the key components for the Beyond 5G/6G, and optical satellite communication is a fundamental technology to enable high-capacity communications. It is affected by interruptions of optical communications due to clouds on the communication link. A satellite can mitigate the interruption by switching its destination ground station to the other communication available station, though it brings additional delays in establishing optical links. In this study, we propose a ground station selection method using reinforcement learning algorithms to realize a fast and stable satellite-terrestrial optical communication system. We introduce two multi-armed bandit algorithms, Q-learning and Deep Q-learning, for the proposed method. We evaluate them using actual data of the optical satellite communication availability. Our simulation results show that the proposed method with deep Q-learning has the best average throughput. The proposed scheme efficiently follows changes in the state of communication links, and it becomes even better than fixed to ideal best link.

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“…Similarly, the A2A links are UAV to UAV (U2U), UAV to HAPs (U2H), and UAV to low earth orbit (LEO) satellites (U2S). UAVs can directly communicate with satellites [42], particularly with GPS, to localize themselves in global coordinates. LAP UAVs communicate with the BS using U2BS downlinks.…”

Section: Flying Ad Hoc Networkmentioning

confidence: 99%

Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks

Arafat

Moh

2022

Electronics

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A flying ad hoc network (FANETs), also known as a swarm of unmanned aerial vehicles (UAVs), can be deployed in a wide range of applications including surveillance, monitoring, and emergency communications. UAVs must perform real-time communication among themselves and the base station via an efficient routing protocol. However, designing an efficient multihop routing protocol for FANETs is challenging due to high mobility, dynamic topology, limited energy, and short transmission range. Recently, owing to the advantages of multi-objective optimization, Q-learning (QL)-based position-aware routing protocols have improved the performance of routing in FANETs. In his article, we provide a comprehensive review of existing QL-based position-aware routing protocols for FANETs. We rigorously address dynamic topology, mobility models, and the relationship between QL and routing in FANETs, and extensively review the existing QL-based position-aware routing protocols along with their advantages and limitations. Then, we compare the reviewed protocols qualitatively in terms of operational features, characteristics, and performance metrics. We also discuss important open issues and research challenges with potential research directions.

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XiA: Send-It-Anyway Q-Routing for 6G-Enabled UAV-LEO Communications

Cited by 18 publications

References 21 publications

UAV Ad Hoc Network Routing Algorithms in Space–Air–Ground Integrated Networks: Challenges and Directions

UAV Ad Hoc Network Routing Algorithms in Space–Air–Ground Integrated Networks: Challenges and Directions

Applying reinforcement learning algorithms to ground station selection in satellite-terrestrial optical communication

Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks

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