Multi-UAV Delay Optimization in Edge Caching Networks: A Mean Field Game Approach

Xue, Kaiyuan; Li, Lixin; Yang, Fan; Zhang, Huisheng; Xu, Li; Han, Zhu

doi:10.1109/wocc.2019.8770639

Cited by 9 publications

(8 citation statements)

References 18 publications

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“…The primary problem with ultra-dense UAV based communication systems is the demand for extremely low latency. Consequently, in [91], the authors aim to lower the overall latency of the system by manipulating the UAVs' 3D location. It turns out that the delay may be reduced in two ways: improving the content-cache-hit-rate by characterising the popularity dynamics of the material, and increasing the backhaul capacity to minimise the delay by minimising inter-UAV interference with flight control.…”

Section: End-to-end Latencymentioning

confidence: 99%

Ultra-Dense Networks: Taxonomy and Key Performance Indicators

et al. 2022

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One major influence on the future deployment of cellular networks will be a continuous increase in traffic inside mobile broadband systems. Moreover, traditional macrocell-based mobile communication networks will struggle to keep up with the enormous expansion in the demand for communications services in the future. Densification of networks is required if we are to meet the comprehensive needs for end terminals for a wide range of applications. One of the leading concepts in this competitive environment is the Ultra-Dense Network (UDN) where the access nodes and/or the communication links per unit area are densified, with the aim of improving overall network performance. The location of the UDN nodes meets the criteria for symmetry with a high probability. Ultra-dense cell deployment aims to reduce the physical distance between the transmitter and receiver in order to boost system performance and generally optimize the values of a wide variety of key performance indicators (KPIs). This paper aims to provide a taxonomy of UDNs and specifically of UDN-related KPIs. Initially, we address the complex questions “What is the current understanding of what ultra-dense networks are and what they should be, and how can we measure their performance?” by shedding light on the fundamental characteristics of UDNs.

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Section: End-to-end Latencymentioning

confidence: 99%

Ultra-Dense Networks: Taxonomy and Key Performance Indicators

et al. 2022

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“…In [115], a game theoretical approach is adopted to study the caching problems. In this system, multiple UAVs flying with the cache unit aim to minimize the delay of content downloading of mobile users.…”

Section: Uav-assisted Cachingmentioning

confidence: 99%

Air-Ground Integrated Mobile Edge Networks: A Survey

Zhang

et al. 2020

IEEE Access

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With proliferation of smart devices and wireless applications, the recent few years have witnessed data surge. These massive data needs to be stored, transmitted, and processed in time to exploit their value for decision making. Conventional cloud computing requires transmission of massive amount of data in and out of core network, which can lead to longer service latency and potential traffic congestion. As a new platform, mobile edge computing (MEC) moves computation and storage resources to edge network in proximity to the data source. With MEC, data can be processed locally, and thus mitigate issues of latency and congestion. However, it is very challenging to reap the benefits of MEC everywhere due to geographic constraints, expensive deployment cost, and immoveable base stations. Because of easy deployment and high mobility of unmanned aerial vehicles (UAVs), air-ground integrated mobile edge networks (AGMEN) is proposed, where UAVs are employed to assist the MEC network. Such an AGMEN expects to provide MEC services ubiquitously and reliably. In this article, we first introduce the characteristics and components of UAV. Then, we will review the applications, key challenges, and current research technologies of AGMEN, from perspectives of communication, computation, and caching, respectively. Finally, we will discuss some essential research directions for AGMEN.

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“…For our considered ultra-dense DSCs network, the first condition can be meet [30]- [34]. And in our system, we assume that all DSCs have the same spaces of the state and the control, which is rational in practical as the DSCs should obey the same flight rules.…”

Section: Mfg Approach For Dense Dscs a Mean Field And Mfamentioning

confidence: 99%

AI-Aided Downlink Interference Control in Dense Interference-Aware Drone Small Cells Networks

Zhang

Xue

et al. 2020

IEEE Access

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Recently, drone small cells (DSCs) has been brought into significant focus, which is the one key enabler for potentially facilitating terrestrial wireless communication systems. Meanwhile, in ultradense unmanned networks, artificial intelligence (AI) has been a useful and efficient tool for control and management of the multi-agents. This paper investigates a downlink interference control problem in ultra-dense unmanned networks with AI-aided approach, that each DSC can adjust its altitude to increase the data-rate. This problem is formulated as a mean field game (MFG) framework, an AI-aided method to make decisions. In this framework, each DSC controls its velocity to minimize the cost over a period, where the cost function is composed by the data-rate and height adjusting consumption. Meanwhile, in this model, we adopt the mean-field approximation (MFA) approach to derive the interference introduced from a large number of DSCs. Besides, the control strategy is described and explained by using the related Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations, respectively. Thus, a finite difference algorithm is proposed to solve the coupled partial differential equations, which can obtain the optimal altitude control strategy. The algorithm outputs show the optimal behaviors of DSCs in different environment scenarios. In additon, the simulation results verify that the proposed control strategy has better average signal to interference plus noise ratio (SINR) compared with the baseline method. INDEX TERMS Artificial intelligence, drone small cell, downlink interference control, mean field game, ultra-dense unmanned networks.

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Multi-UAV Delay Optimization in Edge Caching Networks: A Mean Field Game Approach

Cited by 9 publications

References 18 publications

Ultra-Dense Networks: Taxonomy and Key Performance Indicators

Ultra-Dense Networks: Taxonomy and Key Performance Indicators

Air-Ground Integrated Mobile Edge Networks: A Survey

AI-Aided Downlink Interference Control in Dense Interference-Aware Drone Small Cells Networks

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