Fairness-Aware Link Optimization for Space-Terrestrial Integrated Networks: A Reinforcement Learning Framework

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

doi:10.1109/access.2021.3082862

Cited by 19 publications

(9 citation statements)

References 47 publications

(55 reference statements)

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“…The association nodes could be ground base stations or other UAVs acting as IAB nodes. In [120], the authors considered the problem where low-Earth Orbits provide backhaul connectivity to UAVs. The authors formulated the problem of maximizing user fairness and minimizing of all terrestrial base stations as a multi-armed bandit problem that can be solved using Q-Learning.…”

Section: B Integrated Access and Backhaulmentioning

confidence: 99%

“…• 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%

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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: B Integrated Access and Backhaulmentioning

confidence: 99%

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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“…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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“…In [6], the authors propose an energy-efficient UAV path planning based on reinforcement learning and satisfaction algorithms. To maximize the throughput of an aerial network, learningbased mechanisms are implemented in [9], [13]. In [14], the learning algorithms are surveyed in UAV-assisted SAGINs.…”

Section: Related Workmentioning

confidence: 99%

HAPS-UAV-Enabled Heterogeneous Networks: A Deep Reinforcement Learning Approach

Arani¹,

Hu²,

Zhu³

2023

Preprint

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The integrated use of non-terrestrial network (NTN) entities such as the high-altitude platform station (HAPS) and low-altitude platform station (LAPS) has become essential elements in the space-air-ground integrated networks (SAGINs). However, the complexity, mobility, and heterogeneity of NTN entities and resources present various challenges from system design to deployment. This paper proposes a novel approach to designing a heterogeneous network consisting of HAPSs and unmanned aerial vehicles (UAVs) being LAPS entities. Our approach involves jointly optimizing the three-dimensional trajectory and channel allocation for aerial base stations, with a focus on ensuring fairness and the provision of quality of service (QoS) to ground users. Furthermore, we consider the load on base stations and incorporate this information into the optimization problem. The proposed approach utilizes a combination of deep reinforcement learning and fixed-point iteration techniques to determine the UAV locations and channel allocation strategies. Simulation results reveal that our proposed deep learning-based approach significantly outperforms learningbased and conventional benchmark models.

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Fairness-Aware Link Optimization for Space-Terrestrial Integrated Networks: A Reinforcement Learning Framework

Cited by 19 publications

References 47 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

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

HAPS-UAV-Enabled Heterogeneous Networks: A Deep Reinforcement Learning Approach

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