Towards a 3-tiered space-air-ground network with reinforcement learning

Ndong, Massa; Hayajneh, Mohammad; Ali, Najah Abu; Alkobaisi, Shayma

doi:10.1016/j.jksuci.2022.03.025

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…In another study, DP is used to solve optimization problems in UAV communication within SAGIN [18] . A three-tiered device-to-device (D2D) architecture comprising UAVs, unmanned ground vehicles (UGVs), and satellite network links is proposed to ensure reliable communication links.…”

Section: A Dynaminc Programmingmentioning

confidence: 99%

“…The application of DP includes calculating optimal strategies using RL based on global information such as flight operations, network traffic monitoring, and extra support messages. It also involves computing state transition probabilities, proposing an RL-based algorithm to calculate state transition probabilities in D2D communication, and employing DP to address the real-time reliability issue in continuous data packet transmission [18] .…”

Section: A Dynaminc Programmingmentioning

confidence: 99%

See 1 more Smart Citation

Resource Allocation for Space-Air-Ground Integrated Networks: A Comprehensive Review

Liang,

Yang,

Zhang

et al. 2024

J. Commun. Inf. Netw.

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The space-air-ground integrated networks (SAGIN) has emerged as a critical paradigm to address the growing demands for global connectivity and enhanced communication services. This paper gives a thorough review of the strategies and methodologies for resource allocation within SAGIN, focusing on the challenges and solutions within its complex structure. With the advent of technologies such as 6G, the dynamics of resource optimization have become increasingly complex, necessitating innovative approaches for efficient management. We examine the application of mathematical optimization, game theory, artificial intelligence (AI), and dynamic optimization techniques in SAGIN, offering insights into their effectiveness in ensuring optimal resource distribution, minimizing delays, and maximizing network throughput and stability. The survey highlights the significant advances in AI-based methods, particularly deep learning and reinforcement learning, in tackling the inherent challenges of SAGIN resource allocation. Through a critical review of existing literature, this paper categorizes various resource allocation strategies, identifies current research gaps, and discusses potential future directions. Our findings highlight the crucial role of integrated and intelligent resource allocation mechanisms in realizing the full potential of SAGIN for Manuscript

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Section: A Dynaminc Programmingmentioning

confidence: 99%

Section: A Dynaminc Programmingmentioning

confidence: 99%

Resource Allocation for Space-Air-Ground Integrated Networks: A Comprehensive Review

Liang,

Yang,

Zhang

et al. 2024

J. Commun. Inf. Netw.

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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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“…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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Towards a 3-tiered space-air-ground network with reinforcement learning

Cited by 4 publications

References 27 publications

Resource Allocation for Space-Air-Ground Integrated Networks: A Comprehensive Review

Resource Allocation for Space-Air-Ground Integrated Networks: A Comprehensive Review

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

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

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