Deep Q-Learning Aided Networking, Caching, and Computing Resources Allocation in Software-Defined Satellite-Terrestrial Networks

Qiu, Chao; Yao, Haipeng; Yu, F. Richard; Xu, Fangmin; Zhao, Chenglin

doi:10.1109/tvt.2019.2907682

Cited by 172 publications

(58 citation statements)

References 41 publications

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“…An SDN-enabled satellite-terrestrial network is proposed by Qiu et al [234]. The goal of this integrated network is to dynamically manage networks, computing resources, caching, and jointly orchestrated them.…”

Section: F Sdn-based Satellite Communicationsmentioning

confidence: 99%

Softwarization of UAV Networks: A Survey of Applications and Future Trends

et al. 2020

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Unmanned Aerial Vehicles (UAVs) have become increasingly important in assisting 5G and beyond 5G (B5G) mobile networks. Indeed, UAVs have all the potentials to both satisfy the ever-increasing mobile data demands of such mobile networks and provide ubiquitous connectivity to different kinds of wireless devices. However, the UAV assistance paradigm faces a set of crucial issues and challenges. For example, the network management of current UAV-assisted systems is time consuming, complicated, and carried out manually, thus causing a multitude of interoperability issues. To efficiently address all these issues, Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two promising technologies to efficiently manage and improve the UAV assistance for the next generation of mobile networks. In the literature, no clear guidelines are describing the different use cases of SDN and NFV in the context of UAV assistance to terrestrial networks, including mobile networks. Motivated by this fact, in this survey, we guide the reader through a comprehensive discussion of the main characteristics of SDN and NFV technologies. Moreover, we provide a thorough analysis of the different classifications, use cases, and challenges related to UAV-assisted systems. We then discuss SDN/NFV-enabled UAV-assisted systems, along with several case studies and issues, such as the involvement of UAVs in cellular communications, monitoring, and routing, to name a few. We furthermore present a set of open research challenges, high-level insights, and future research directions related to UAV-assisted systems.

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Section: F Sdn-based Satellite Communicationsmentioning

confidence: 99%

Softwarization of UAV Networks: A Survey of Applications and Future Trends

et al. 2020

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“…A MEC based missioncritical wireless sensor network architecture and a kind of centralized computing resource management strategy were studied in [17]. Qiu et al in [18] explained how to manage and orchestrate resources jointly in software-defined satellite-terrestrial networks. Ye and Li in [19] developed a decentralized resource allocation mechanism for V2V communications based on deep reinforcement learning.…”

Section: B Computational Offloading and Resource Allocation Schemes mentioning

confidence: 99%

Regional Intelligent Resource Allocation in Mobile Edge Computing Based Vehicular Network

Wang

2020

IEEE Access

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The advancement of 5G technology has brought the prosperous development of Internet of Vehicles (IoV). IoV services are not only computational intensive but also extremely sensitive to the delay. As a promising computing paradigm, mobile edge computing (MEC) can be applied to IoV scenarios. However, due to the limited resources of a single MEC server, it is difficult to cope with the suddenly increased computation loads caused by emergencies, or the intensive resource requests from busy regions. Therefore, we propose a novel regional intelligent management vehicular system with dual MEC planes, in which MEC servers in the same region cooperate with each other to achieve resource sharing. We classify computing tasks into different types according to their delay tolerances and focus on the optimization problem of resource allocation for different type tasks. And then, we design a resource allocation algorithm based on deep reinforcement learning, which can adapt to the changeable MEC environment to process high-dimensional data. Simulation results confirm that our proposed scheme is feasible and effective. INDEX TERMS Internet of vehicles, mobile edge computing, deep reinforcement learning, resource allocation, delay tolerance.

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“…Compared to conventional reinforcement learning, it could be applied to high-dimensional state spaces problem. Thus, there are many researches focusing on this topic [20], [21]. Table 1 summarizes the AI-based approaches used in resource management.…”

Section: B Ai For Resource Allocation Problemmentioning

confidence: 99%

AI Inspired Intelligent Resource Management in Future Wireless Network

Yang

Xiao

2020

IEEE Access

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In order to improve network performance, including reducing computation delay, transmission delay and bandwidth consumption, edge computing and caching technologies are introduced to the fifth-generation wireless network (5G). However, the volume of edge resources is limited, while the number and complexity of tasks in the network are increasing sharply. Therefore, how to provide the most efficient service for network users with limited resources is an urgent problem to be solved. Thus, improving the utilization rate of communication, computing and caching resources in the network is an important issue. The diversification of network resources brings difficulties to network management. The joint resource allocation problem is difficult to be solved by traditional approaches. With the development of Artificial Intelligence (AI) technology, these AI algorithms have been applied to joint resource allocation problems to solve complex decision-making problems. In this paper, we first summarize the AI-based joint resources allocation schemes. Then, an AI-assisted intelligent wireless network architecture is proposed. Finally, based on the proposed architecture, we use deep Q-network (DQN) algorithm to figure out the complex and high-dimensional joint resource allocation problem. Simulation results show that the algorithm has good convergence characteristics, proposed architecture and the joint resource allocation scheme achieve better performance compared to other resource allocation schemes. INDEX TERMS Artificial intelligence, deep Q-network, resource management, edge computing and caching, the fifth-generation wireless network (5G).

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Deep Q-Learning Aided Networking, Caching, and Computing Resources Allocation in Software-Defined Satellite-Terrestrial Networks

Cited by 172 publications

References 41 publications

Softwarization of UAV Networks: A Survey of Applications and Future Trends

Softwarization of UAV Networks: A Survey of Applications and Future Trends

Regional Intelligent Resource Allocation in Mobile Edge Computing Based Vehicular Network

AI Inspired Intelligent Resource Management in Future Wireless Network

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