Deep reinforcement learning‐based beam Hopping algorithm in multibeam satellite systems

Hu, Xin; Liu, Shuaijun; Wang, Yipeng; Xu, Lexi; Zhang, Yuchen; Wang, Cheng; Wang, Weidong

doi:10.1049/iet-com.2018.5774

Cited by 49 publications

(10 citation statements)

References 26 publications

(54 reference statements)

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“…Overview UAV Satellite Multi ML [199]- [201] SL [207]- [210] UL [207], [208] RL [221]- [224], [234], [235] Resource Allocation [226]- [228], [243]- [251] Beam Hopping [229], [249], [272] IoT [227], [228], [250], [273] MEC [32], [252] HO Management [253] Interference Management [254], [255] Others Channel Modeling [256]- [258] Remote Sensing [259]- [264] Traffic Classification [265]- [267] Anti Jamming [268]- [271] MEC [144], [225], [274], [275] IoT [144], [274], [276] Others [277], [278] 3D Placement and Trajectory Design [58], [231]- [233], [279]-…”

Section: Ml-poweredmentioning

confidence: 99%

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Solanki¹,

Chatzinotas²,

Kodheli³

et al. 2021

Preprint

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Non-terrestrial networks (NTNs) traditionally had certain limited applications. However, the recent technological advancements opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting its relevance to 5G networks and essentially, how it will play a pivotal role for the development of 6G and beyond wireless networks. The survey discusses important features of NTNs integration into TNs by delving into the new range of services and use cases, various architectures, and new approaches being adopted to develop new wireless ecosystem. Our survey includes the major progresses and outcomes from academic research as well as industrial efforts. We first start with introducing the relevant 5G use cases and general integration challenges such as handover and deployment difficulties. Then, we review the NTNs operations in mmWave and their potential for internet of things (IoT). Further, we discuss the significance of mobile edge computing (MEC) and machine learning (ML) in NTNs by reviewing the relevant research works. Furthermore, we also discuss the corresponding higher layer advancements and relevant field trials/prototyping at both academic and industrial levels. Finally, we identify and review 6G and beyond application scenarios, novel architectures, technological enablers, and higher layer aspects pertinent to NTNs integration.

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Section: Ml-poweredmentioning

confidence: 99%

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Solanki¹,

Chatzinotas²,

Kodheli³

et al. 2021

Preprint

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show abstract

“…In [21], Han et al focused on the delay fairness of each cell in the BH system, and a delay fairnessoriented BH algorithm was proposed. In [22]- [24], BH was combined with deep learning or deep reinforcement learning, which provides a novel optimization method to obtain the BH transmission scheme.…”

Section: Related Workmentioning

confidence: 99%

Optimization Method of Dynamic Beam Position for LEO Beam-Hopping Satellite Communication Systems

Tang

Bian

et al. 2021

IEEE Access

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The beam-hopping (BH) technology applied to low earth orbit (LEO) satellite communication networks is a superior choice, but the long transmission delay partly caused by data packets waiting in the queue of satellite transponders will seriously affect the user experience. To shorten the packet queueing delay, in this paper, we propose an optimization method of dynamic beam position division for LEO BH satellite communication systems. Firstly, we analyze the packet queueing delay problem in BH satellites to find out the factors related to the queueing delay, and we find that the number of beam positions is negatively correlated with the queueing delay. Then, we turn the beam position division problem into a p-center problem to try to cover all users with the least number of beam positions. The beam positions among the footprint of LEO satellites are determined dynamically by the user distribution and the traffic distribution. Finally, the performance evaluation of the proposed optimization method is carried out in realtime and the simulation shows that the beam position division optimized system we proposed can shorten the queueing delay up to 40% compare to the benchmark system without sacrificing throughput.INDEX TERMS LEO satellite communication system, beam-hopping, beam position optimization, packet queueing delay, p-center problem

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“…Then, in [15] a continuous DRL architecture is used to allocate power in a 30-beam HTS, showing a 1,300-times speed increase with respect to a comparable GA approach. Finally, in [16] DRL is used to carry out beam-hopping tasks in a 10-beam and 37-cell scenario, showing a stable performance throughout a 24-hour test case.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Prior studies mostly focus on static datasets; only works [14], [15], and [16] prove the usefulness of DRL throughout 24hour operation scenarios. The desired execution frequency alongside the algorithm convergence time might impose hard constraints on the use of specific candidates, therefore it is important to quantify the algorithm performance in dynamic environments.…”

Section: Continuous Execution Performancementioning

confidence: 99%

Artificial Intelligence Algorithms for Power Allocation in High Throughput Satellites: A Comparison

Luis

Pachler

Guerster

et al. 2020

2020 IEEE Aerospace Conference

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Automating resource management strategies is a key priority in the satellite communications industry. The future landscape of the market will be changed by a substantial increase of data demand and the introduction of highly flexible communications payloads able to operate and reconfigure hundreds or even thousands of beams in orbit. This increase in dimensionality and complexity puts the spotlight on Artificial Intelligence-based dynamic algorithms to optimally make resource allocation decisions, as opposed to previous fixed policies. Although multiple approaches have been proposed in the recent years, most of the analyses have been conducted under assumptions that do not entirely reflect operation scenarios. Furthermore, little work has been done in thoroughly comparing the performance of different algorithms.In this paper we compare some of the recently proposed dynamic resource allocation algorithms under realistic operational assumptions, addressing a specific problem in which power needs to be assigned to each beam in a multibeam High Throughput Satellite (HTS). We focus on Genetic Algorithms, Simulated Annealing, Particle Swarm Optimization, Deep Reinforcement Learning, and hybrid approaches. Our multibeam operation scenario uses demand data provided by a satellite operator, a full radio-frequency chain model, and a set of hardware and time constraints present during the operation of a HTS. We compare these algorithms focusing on the following characteristics: time convergence, continuous operability, scalability, and robustness. We evaluate the performance of the algorithms against different test cases and make recommendations on the approaches that are likely to work better in each context.

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Deep reinforcement learning‐based beam Hopping algorithm in multibeam satellite systems

Cited by 49 publications

References 26 publications

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Optimization Method of Dynamic Beam Position for LEO Beam-Hopping Satellite Communication Systems

Artificial Intelligence Algorithms for Power Allocation in High Throughput Satellites: A Comparison

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