Caching in the Sky: Proactive Deployment of Cache-Enabled Unmanned Aerial Vehicles for Optimized Quality-of-Experience

By Mingzhe Chen, Mohammad Mozaffari, Walid Saad, Changchuan Yin, Merouane Debbah, Choong Seon Hong. IEEE Journal on Selected Areas in Communications, 2016

671 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/4050270f1afe99c3c7fdb6ec10c84b40031e9f0b

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Citations in the corpus

Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles (2020, methodology): “D2D communication has been widely applied in wireless networks such as cellular networks [18], [19], and ad hoc networks [20].”
Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT (2019, methodology): “In wireless communications, UAV has been applied in various scenarios, such as nonorthogonal multiple access (NOMA) networks [10], mmWave communications [11] and caching [12] [13].”
A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, background): “Reference [177] focuses on the deployment of cacheenabled Unmanned Aerial Vehicles (UAVs) in a Cloud Radio Access Network (C-RAN) to optimize users’ QoE and UAVs’ transmit power.”
UAV Communications for 5G and Beyond: Recent Advances and Future Trends (2019, background): “In [130], the problem of proactive deployment of cache-enabled UAVs in a cloud radio access network was explored to minimize the transmit power of the UAVs.”
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, methodology): “In [73], the joint optimization of content placement, user association, and unmanned aerial vehicles’ positions is studied, aiming at minimizing the total transmit power of UAVs while satisfying the requirement of user quality of experience.”
Space/Aerial-Assisted Computing Offloading for IoT Applications: A Learning-Based Approach (2019, neutral): “In [22], Chen et al.”
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence (2019, neutral): “cles (UAVs) became very popular [16]–[18].”
Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail (2018, methodology): “In our future work, we will extend the current framework to other use cases such as autonomous aerial vehicles (UAVs) [24], [25] and industrial control [26].”
A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022, methodology): “Additionally, Chen et al. [125] developed a UAV-based framework for providing service to users where the methods they propose use ML to separate user behavior into multiple patterns.”
A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems (2018, background): “, and number of the obstacles, one must consider the randomness associated with the LoS and NLoS links. As a result, many of the existing literature on UAV communication (e.g., [8], [15], [37], [64], [90], [99], [113]–[117]) adopted the probabilistic path loss model given in [11], and [29]. As discu”
Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, background): “rithm proposed in [188], since a centralized approach only”
Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach (2018, neutral): “[32] proposed a”
Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective (2018, methodology): “The proactive caching technique was considered in [84], [85] for UAV-enabled communication, where the proactive deployment of cache-enabled UAVs was studied in [84] to optimize the quality-ofexperience (QoE) of ground users, while caching at the ground users was leveraged in [85] to overcome the UAV”
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A Survey of Channel Modeling for UAV Communications (2018, background): “It increases the number of users in cellular–assisted UAV network in [92] and UAV for data caching purpose in [93].”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, methodology): “Simulation result from [171] showing transmit power as the number of users varies.”