Caching in the Sky: Proactive Deployment of Cache-Enabled Unmanned Aerial Vehicles for Optimized Quality-of-Experience
Open accessAbstract
In this paper, the problem of proactive deployment of cache-enabled unmanned aerial vehicles (UAVs) for optimizing the quality-of-experience (QoE) of wireless devices in a cloud radio access network is studied. In the considered model, the network can leverage human-centric information, such as users' visited locations, requested contents, gender, job, and device type to predict the content request distribution, and mobility pattern of each user. Then, given these behavior predictions, the proposed approach seeks to find the user-UAV associations, the optimal UAVs' locations, and the contents to cache at UAVs. This problem is formulated as an optimization problem whose goal is to maximize the users' QoE while minimizing the transmit power used by the UAVs. To solve this problem, a novel algorithm based on the machine learning framework of conceptor-based echo state networks (ESNs) is proposed. Using ESNs, the network can effectively predict each user's content request distribution and its mobility pattern when limited information on the states of users and the network is available. Based on the predictions of the users' content request distribution and their mobility patterns, we derive the optimal locations of UAVs as well as the content to cache at UAVs. Simulation results using real pedestrian mobility patterns from BUPT and actual content transmission data from Youku show that the proposed algorithm can yield 33.3% and 59.6% gains, respectively, in terms of the average transmit power and the percentage of the users with satisfied QoE compared with a benchmark algorithm without caching and a benchmark solution without UAVs.
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To introduce potential fairness and reduce training accuracy variance, local devices having a high empirical loss are emphasized by assigning higher relative weight in the fairness enabled FedAvg.
“To introduce potential fairness and reduce training accuracy variance, local devices having a high empirical loss (local loss function) are emphasized by assigning higher relative weight in the fairness enabled FedAvg.”
From Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism (2020) · cites this paper as [8]
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Self-citation
- Authors
- Chen, M., Mozaffari, M., Saad, W., Yin, C., Debbah, M., & Hong, C. S.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- Virginia Tech (US)
- Kyung Hee University (KR)
- Supélec (FR)
- Venue
- IEEE Journal on Selected Areas in Communications
- Published
- 2017-05
- Volume
- 35
- Issue
- 5
- Pages
- 1046-1061
- ISSN
- 0733-8716 (ISSN-L)
- OpenAlex
- W2528788999
- Cited by
- 742
- References
- 37
- Citations by year
- 2026: 9 · 2025: 24 · 2024: 33 · 2023: 57 · 2022: 69 · 2021: 113 · 2020: 154 · 2019: 160 · 2018: 86 · 2017: 37
- FWCI
- 110.13
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Journal on Selected Areas in Communications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61671086, 61629101, 60972073)
- ERC Starting Grant (305123 MORE)
- U.S. National Science Foundation (IIS-1633363, CNS-1460316, CNS-1513697)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Physical Sciences › Computer Science › Computer Networks and Communications › Age of Information Optimization
- Keywords
- quality of experience, cloud radio access network, user mobility prediction, echo state networks, transmit power minimization
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