Proactive Deployment of Cache-Enabled Aerial Base Stations for Optimized Energy-Delay Cost

Cheng, Shao-Hung; Shih, Yen-Ting; Chang, Ko-Chin

doi:10.1109/pimrc54779.2022.9977871

Cited by 3 publications

(3 citation statements)

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“…The deployment and placement of a UAP as a function of the users' parameters like (i) user density and mobility [3]- [5], [11], [16], (ii) end user throughput [6], (iii) video requests [12], and (iv) QoS [15], [17]- [19] requirements have also been studied in the literature. It is illustrated that mobile users experience poor QoS, i.e., high delay if the time to deploy a UAP is too high [5].…”

Section: A Literature Surveymentioning

confidence: 99%

“…UAP deployment as a function of the time-averaged request arrival and download rates is illustrated to minimize the video streaming delay in [18], [22]. User mobility and energy-efficiency-aware UAP movement are illustrated to limit delay [17] and the average transmit power [19]. Finally, caching of contents in a high-altitude platform using federated learning is implemented for estimating content popularity in [23].…”

Section: A Literature Surveymentioning

confidence: 99%

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Resource-Efficient QoS-Aware Video Streaming Using UAV-Assisted Networks

Bhar,

Ghosh,

Agrell

2024

IEEE Trans. Cogn. Commun. Netw.

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Emerging video services are associated with stringent quality-of-service (QoS) and high data-rate requirements. Moreover, the presence of data-rate-hungry mobile users in future networks necessitate sophisticated design strategies. The deployment of unmanned aerial vehicle access point (UAP)assisted networks (UANs) has been proposed to ensure high data-rates to mobile users. Moreover, UAPs can be equipped with energy-efficient caches to facilitate video delivery with stringent QoS. However, the mobility of users and UAPs may cause temporal variations in the QoS experienced by users. This paper conducts an extensive performance evaluation of a UAN, by studying the effect of user behavior, mobility of users and UAPs, and a temporal variation of video popularity on the QoS. The QoS is measured in terms of the delay experienced by the users. To that end, a time-dependent queueing model and its associated fluid approximation models are derived, which are illustrated to be reasonably accurate in an appropriate asymptotic regime. A detailed analysis of these models reveals that low delay, i.e., high QoS, can be ensured in UANs. Finally, a reinforcement-learning (RL) approach based on these models is utilized to minimize the number of deployed UAPs and the playout buffer size while guaranteeing a certain QoS.

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Section: A Literature Surveymentioning

confidence: 99%

Section: A Literature Surveymentioning

confidence: 99%

Resource-Efficient QoS-Aware Video Streaming Using UAV-Assisted Networks

Bhar,

Ghosh,

Agrell

2024

IEEE Trans. Cogn. Commun. Netw.

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“…The researchers in [25], [26] define the quality of experience (QoE) and present joint UAV deployment and caching placement strategies to maximize mean opinion score [25] or maximize the average content delay index [26]. In [27], [28], the authors design joint UAV deployment and content placement algorithms to minimize the sum cost of energy consumption and file delivery delay [27] or maximize secure throughput [28].…”

Section: A Literature Reviewmentioning

confidence: 99%

Content Fetching Delay Optimization-Based Caching and Resource Allocation for UAV-Enabled Networks

Bayessa,

Chai,

Liang

et al. 2024

IEEE Access

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In this work, we investigate content caching problem in unmanned aerial vehicle (UAV)enabled networks where UAVs are allowed to store user requested content so as to enhance the content transmission performance of users. To predict user content request, we propose a bidirectional long shortterm memory-based algorithm. Then, the content transmission delay of users is examined and the UAV deployment, content caching and resource allocation problem is formulated as an overall content fetching delay minimization problem. As the formulated problem is difficult to solve, we transform it into two subproblems which are solved iteratively. To tackle the UAV deployment and content caching subproblem, we first design a modified K-means-based clustering scheme and then propose a UAV deployment strategy by using quadratic transformation. To solve subcarrier and power allocation subproblem, we apply Lagrangian dual method to determine power allocation strategy and propose a Kuhn-Munkres algorithm-based subcarrier allocation strategy. Simulation results show that our approach achieves approximately 11.1% lower overall content fetching delay compared to the existing algorithm.INDEX TERMS Unmanned aerial vehicles, user clustering, UAV deployment, content caching, resource allocation.

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