Re-envisioning Space-Air-Ground Integrated Networks: Reinforcement Learning for Link Optimization

Arani, Atefeh Hajijamali; Hu, Peng; Zhu, Yeying

doi:10.1109/icc42927.2021.9500978

Cited by 13 publications

(11 citation statements)

References 21 publications

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“…• In terms of energy considerations, a fair number of works presented energy-efficient factors and constraints in their formulations such as battery capacity [103], energy harvesting [112,150,157], propulsion energy [113,139], energy quanta [136,139] and others [95,96,140]. Upon analyzing Table 5, we notice that more attention was paid to 3D environments with more realistic deployment scenarios where multiple non terrestrial platforms coordinate together to provide multi-user access control [116] in NTNs, space-air-ground integrated link optimization [151,153], maximizing end-to-end data rate [117] and others [154,155].…”

Section: Qualitative Analysis: Simulation Realismmentioning

confidence: 99%

Reinforcement Learning in the Sky: A Survey on Enabling Intelligence in NTN-Based Communications

et al. 2023

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Non terrestrial networks (NTN) involving 'in the sky' objects such as low-earth orbit satellites, high altitude platform systems (HAPs) and Unmanned Aerial Vehicles (UAVs) are expected to be integral components of next generation cellular systems. With the deployment of 5G services and beyond, NTNs are leveraged to assist as aerial base stations in providing ubiquitous network connectivity and service to ground users or be deployed as aerial users connected to the cellular network. NTN-aided wireless communication offers multiple benefits such as mobility, flexibility, resistance to ground physical attacks and wide coverage. However, due to their limited resources and the current design of terrestrial cellular systems that do not account for aerial users, and other restrictions such as service requirements, limited available power and storage resources on high-throughput satellites, resource allocation, location of the high altitude platform base station and the flight trajectory of the UAVs need to be intelligently controlled to satisfy various objectives both from an aerial base station and overall network perspectives. To achieve this, many works have explored Reinforcement Learning (RL) techniques to allow aerial platforms in non-terrestrial networks to learn from past observations and achieve some optimal control policy. In this paper and differently from prior surveys, we contribute a comprehensive review of the control objectives required by non-terrestrial platforms that have been solved using RL formulations. We provide an up-to-date overview of the latest applications of RL techniques for different NTN-aided wireless communication aspects. The survey focuses on Markov Decision Process (MDP) formulations in terms of states, actions, and rewards. We synthesize a taxonomy from the surveyed literature and provide a comprehensive representation of the current usages of RL in NTN-aided wireless communications. A qualitative analysis of the level of realism achieved in the works presented in the literature is provided based on several factors that pertain to the simulation environment, station deployment setting, wireless channel assumption, and energy considerations. We also curate a list of challenges that remain to be considered by the research community in order to achieve more efficient deployments and close the simulation-to-reality gap.

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Section: Qualitative Analysis: Simulation Realismmentioning

confidence: 99%

Reinforcement Learning in the Sky: A Survey on Enabling Intelligence in NTN-Based Communications

et al. 2023

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“…There are still issues related to standardization and safety that need to be addressed to ensure seamless integration while guaranteeing safety and privacy preservation. Despite these limitations, continued progress is being made in addressing communication-related issues, such as modeling of UAV-satellite channels [354], as well as addressing backhaul and access resource allocation, trajectory planning [355], etc.…”

Section: Integration Of Multi-tier Heterogeneous Uav Networkmentioning

confidence: 99%

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar

Ahmad

Omeke

et al. 2023

Drones

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Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature.

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“…However, they also have certain limitations. To overcome all the limitations of .5 [72] 2021 Proposed solution 1 1 0 0 2 [73] 2019 Proposed solution 1 1 1 1 4 [74] 2022 Framework 1 1 0.5 2 4.5 [75] 2022 Proposed solution 0 0.5 0 1.5 2 [76] 2019 Proposed solution 1 0.5 1 0 2.5 [77] 2020 Survey 1 0.5 1 2 4.5 [78] 2021 Model 1 0.5 1 2 4.5 [79] 2019 Framework 0 0.5 1 2 3.5 [80] 2021 Model 0 0.5 0.5 1 2 [81] 2020 Proposed solution 1 2021 Proposed solution 1 1 0.5 2 4.5 [90] 2018 Framework 0 0.5 1 2 3.5 [91] 2022 Survey 1 1 0.5 2 4.5 [92] 2021 Survey 1 0.5 1 2 4.5 [93] 2021 Proposed solution 1 0.5 0 2 3.5 [30] 2021 Proposed solution 1 1 0.5 1.5 4 [94] 2017 Survey 1 0.5 0.5 0 2 [95] 2017 Framework 1 0.5 0 0 1.5 [96] 2020 Proposed solution 1 0. In recent years, SAGIN has been used by multiple organizations including SpaceX [134] and Global Information Grid [135].…”

Section: Space-air-ground Integratedmentioning

confidence: 99%

“…Similarly, different authors have defined specific models for either the transmission characteristics or other network characteristics for the betterment of data transmission between the different components of SAGIN [19,30,50,51,57,60,71,78,80,87,97,110,114,119,124]. 4.3.6.…”

Section: Modelmentioning

confidence: 99%

Space-Air-Ground Integrated Network for Disaster Management: Systematic Literature Review

Anjum

Anees

Tariq

et al. 2023

Applied Computational Intelligence and Soft Computing

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The occurrence of any kind of natural disaster will eventually lead to the loss of life and property. Countries where such disasters occur make every effort to monitor such disasters and aid as quickly as possible. However, in some cases, a rescue cannot be sent because no information is available to initiate any type of rescue operation. This is usually because common disaster management systems (DMS) use on board or ground networks to route information from the disaster scene to rescue headquarters (HQ), which in most cases cannot provide the information efficiently. One effective approach is to use satellites in conjunction with existing air-to-ground systems. This study provides a comprehensive and systematic overview of the complexities of the space-air-ground integrated network (SAGIN) in disaster management applications, including different architectures and protocols. The main rationale behind this review is to provide an extensive analysis of existing disaster management systems that are making use of SAGIN. This paper also presents the taxonomy for disaster management systems and challenges. Moreover, this research work also highlights open research issues and challenges for any type of disaster scenario. Our results indicate that several challenges are faced by disaster management systems such as hardware-based challenges, network-based characteristics and communication protocols related challenges, availability and accuracy of imagery data, and security and privacy issues.

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Re-envisioning Space-Air-Ground Integrated Networks: Reinforcement Learning for Link Optimization

Cited by 13 publications

References 21 publications

Reinforcement Learning in the Sky: A Survey on Enabling Intelligence in NTN-Based Communications

Reinforcement Learning in the Sky: A Survey on Enabling Intelligence in NTN-Based Communications

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Space-Air-Ground Integrated Network for Disaster Management: Systematic Literature Review

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