Caching Placement and Resource Allocation for AR Application in UAV NOMA Networks

Wang, Ziduan; Zhang, Tiankui; Liu, Yuanwei; Xu, Wenjun

doi:10.1109/globecom42002.2020.9322556

Cited by 10 publications

(10 citation statements)

References 11 publications

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“…4, respectively. In task caching, the UAV and the mobile edge computing (MEC) server collaborate in providing their computing and caching resources for the EUs [42], [71], [76], [84], [88], [89]. Task caching is actually the caching of data related to a task offloading plan and/or the caching of a completed task with associated data [101].…”

Section: Uav Caching Models and Performance Metrics A Caching Modelsmentioning

confidence: 99%

See 1 more Smart Citation

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

Duong

Kim

Kaleem

et al. 2022

Physical Communication

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Section: Uav Caching Models and Performance Metrics A Caching Modelsmentioning

confidence: 99%

“…[83]- [85], [89], [94], [95], [98], [114]. Meanwhile, inband UAV communications, i.e., overlay mode [44], [51], [57], [64], [67], [73], [81] and underlay mode with interference [8], [42], [43], [46], [50], [52], [59], [61], [63], [68], [71], [75], [77], [82], [86]- [88], [92], [93], [97], [99],…”

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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“…In [173], resource allocation for a UAV-assisted cellular system has been considered for maximizing the quality of experience (QoE) of the users by optimizing the content placement in the cache, location of UAV, and user association. In [174], to minimize the delivery delay, the authors have modelled an optimization problem for UAV deployment, caching placement, and power allocation of NOMA as a Stackelberg game. However, to minimize the content delivery delay, the authors in [175] have incorporated the Markov decision process for jointly optimising the content placement, user scheduling, and power allocation to NOMA users.…”

Section: A Non Terrestrial Networkmentioning

confidence: 99%

“…Various algorithms researches have been applied to solve the optimization problem of UAV systems which have hardly considered the dynamic networks environment including the movement of UAV. The authors have applied a Markov decision process (MDP) to model caching placement and resource allocation with dynamic UAV locations and content requests [174] [175].…”

Section: A Non Terrestrial Networkmentioning

confidence: 99%

A Survey on Cache-Aided NOMA for 6G Networks

Bepari¹,

Mondal²,

Chandra³

et al. 2022

Preprint

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Contrary to orthogonal multiple-access (OMA), non-orthogonal multiple-access (NOMA) schemes can serve a pool of users without exploiting the scarce frequency or time domain resources. This is useful in meeting the sixth generation (6G) network requirements, such as, low latency, massive connectivity, users' fairness, and high spectral efficiency. On the other hand, content caching restricts duplicate data transmission by storing popular contents in advance at the network edge which reduces 6G data traffic. In this survey, we focus on cache-aided NOMA-based wireless networks which can reap the benefits of both cache and NOMA; switching to NOMA from OMA enables cache-aided networks to push additional files to content servers in parallel and improve the cache hit probability. Beginning with fundamentals of cache-aided NOMA technology, we summarize the performance goals of cache-aided NOMA systems, present the associated design challenges, and categorize related recent literature based on their application verticals. Concomitant standardization activities and open research challenges are highlighted as well.

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“…The work presented in [27] utilizes Q-learning approach to handle the placement of cache and managing resources allocation UAV networks utilizing NOMA. The work in [28] is about the management of dynamic-caching and associated resource allocation utilizing deep reinforcement learning, with an objective to minimize the latency of content. In [29] the authors present a deep Q-learning augmented resource management framework for minimization of the packet loss of the IoT devices.…”

Section: E Related Workmentioning

confidence: 99%