Mobility-Aware Probabilistic Caching in UAV-Assisted Wireless D2D Networks

Chen, Yu-Jia; Liao, Kai-Min; Ku, Meng‐Lin; Tso, Fung Po

doi:10.1109/globecom38437.2019.9013432

Cited by 21 publications

(6 citation statements)

References 16 publications

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“…To deal with the environmental uncertainties, a deep reinforcement learning approach was proposed in [29] to jointly design the caching, scheduling, and UAVs power allocation, and trajectories for latency minimization. Different from the above papers that focused on using UAVs to directly serve users, [25], [26] and [27] worked on cache-aided UAVassisted D2D networks and investigated the caching policy optimization for UAVs and devices as well as the optimization of UAV trajectories.…”

Section: A Related Literature Reviewmentioning

confidence: 99%

Joint Caching, Communication, and Trajectory Optimization in Air-Ground Integrated Wireless Networks With Multiple UAVs and Multiple BSs

Lin,

Huang,

Lee

et al. 2024

IEEE Access

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Using unmanned aerial vehicles (UAVs) to improve the network performance has been widely discussed due to their high agility for deployment and better line-of-sight (LoS) channels. To further improve the efficiency of using UAVs to improve the network, this paper investigates the optimization of cache-enabled air-ground integrated networks where multiple UAVs and base stations (BSs) jointly serve users. Aiming to maximize the minimum user rate of the network, we first formulate two optimization problems. The first problem considers jointly optimizing the user scheduling and association, UAV power allocation, bandwidth allocation, and UAV trajectories when the caching placement is pre-determined, and the second problem further jointly optimizes the caching placement. To solve these two problems, we propose to decompose them into sub-problems, and then solve the sub-problems iteratively to obtain the solutions. The solution approaches of each sub-problem are provided. In addition, convergences of the proposed approaches are analyzed. Simulation results show that our proposed approaches perform well and outperform the reference schemes.INDEX TERMS UAV communications, cache-aided wireless networks, joint caching and communication optimization, UAV trajectory optimization.

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Section: A Related Literature Reviewmentioning

confidence: 99%

Joint Caching, Communication, and Trajectory Optimization in Air-Ground Integrated Wireless Networks With Multiple UAVs and Multiple BSs

Lin,

Huang,

Lee

et al. 2024

IEEE Access

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“…The flying trajectory of the UAVs, which is more complicated with respect to the mobility of the EUs, was studied in [74]. A content is assumed to be cached in a UAV only depending on the popularity of the content.…”

Section: ) Task Cachingmentioning

confidence: 99%

“…The aforementioned channel models enable to simply derive the transmission models based on outband UAV communications [47]- [49], [53], [55], [58], [65], [69], [72], [74], [78], [79],…”

Section: ) Uav Channel and Transmissionmentioning

confidence: 99%

UAV caching in 6G networks: A Survey on models, techniques, and applications

Duong

Kim

Kaleem

et al. 2022

Physical Communication

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“…Kwshavarzian et al [ 111 ] grouped small units into unrelated clusters and modeled the user’s movement between clusters as a Markov chain model to reduce the total energy consumption of content delivery. At the same time, some researchers are studying the user movement model in the unmanned aerial vehicle (UAV) scene [ 112 ].…”

Section: Delivery Processmentioning

confidence: 99%

A Comprehensive Review on Edge Caching from the Perspective of Total Process: Placement, Policy and Delivery

Fan

Wang

et al. 2021

Sensors

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With the explosive growth of smart devices and mobile applications, mobile core networks face the challenge of exponential growth in traffic and computing demand. Edge caching is one of the most promising solutions to the problem. The main purpose of edge caching is to place popular content that users need at the edge of the network, borrow free space to reduce user waiting time, and lighten the network load by reducing the amount of duplicate data. Due to the promising advantages of edge caching, there have been many efforts motivated by this topic. In this paper, we have done an extensive survey on the existing work from our own perspectives. Distinguished from the existing review articles, our work not only investigates the latest articles in this area, but more importantly, covers all the researches of the total process of edge caching from caching placement optimization, policy design, to the content delivery process. In particular, we discuss the benefits of caching placement optimization from the perspective of different stakeholders, detail the delivery process, and conduct in-depth discussions from the five phases, i.e., requested content analysis, user model analysis, content retrieval, delivery, and update. Finally, we put forward several challenges and potential future directions, and hope to bring some ideas for the follow-up researches in this area.

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Mobility-Aware Probabilistic Caching in UAV-Assisted Wireless D2D Networks

Cited by 21 publications

References 16 publications

Joint Caching, Communication, and Trajectory Optimization in Air-Ground Integrated Wireless Networks With Multiple UAVs and Multiple BSs

Joint Caching, Communication, and Trajectory Optimization in Air-Ground Integrated Wireless Networks With Multiple UAVs and Multiple BSs

UAV caching in 6G networks: A Survey on models, techniques, and applications

A Comprehensive Review on Edge Caching from the Perspective of Total Process: Placement, Policy and Delivery

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