Deep Reinforcement Learning for Caching Placement and Content Delivery in UAV NOMA Networks

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

doi:10.1109/wcsp49889.2020.9299784

Cited by 10 publications

(10 citation statements)

References 10 publications

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“…[42], [49], [53], [75], [84], [92], [93], [97] [88] Connectivity [50], [83] CHR [73] LAR [44] NE [60], [62], [98], [99] NoE [54] NoU [67] NoU [59], [63], [77], [80], [87], [95] SR…”

Section: B Taxonomymentioning

confidence: 99%

“…Furthermore, in the context of FXL, the authors in [98]- [100] have combined the caching placement and user association solutions with spectrum resource allocation and power allocation techniques to maximise the NoE and the EE. It is interesting that non-orthogonal multiple access (NOMA) technique [92], [93] can assist the caching placement to minimise the content delivery delay. Meanwhile, the authors in [97] have utilised both caching placement and user association solutions, but without considering any added techniques, in order to minimise the delay.…”

Section: ) Task Cachingmentioning

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%

See 2 more Smart Citations

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

Duong

Kim

Kaleem

et al. 2022

Physical Communication

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“…[42], [49], [53], [75], [84], [92], [93], [97] [88] Connectivity [50], [83] CHR [73] LAR [44] NE [60], [62], [98], [99] NoE [54] NoU [67] NoU [59], [63], [77], [80], [87], [95] SR…”

Section: B Taxonomymentioning

confidence: 99%

Section: ) Task Cachingmentioning

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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“…Furthermore, an improved power allocation strategy named crosslayer based optimal method is employed to maximize the system hit probability [171]. A deep reinforcement learning (DRL) algorithm is proposed for content placement and delivering in a cache-enabling UAV-assisted cellular network [172]. The cache-enabled UAV serves users directly on the availability of the requested file in the cache.…”

Section: A Non Terrestrial Networkmentioning

confidence: 99%

“…Cache-aided UAV was deployed to maximize the sum fairness of the vehicles. In [172], cache-enabled UAV was deployed in a cellular network to assist the delivery of the user requested multimedia contents. Cache-enabled UAV was deployed in a NOMA-based MEC network to minimize the consumption of total energy in [149].…”

Section: A Non Terrestrial Networkmentioning

confidence: 99%

A Survey on Cache-Aided NOMA for 6G Networks

Bepari¹,

Mondal²,

Chandra³

et al. 2022

Preprint

Self Cite

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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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Cache Sharing in UAV-Enabled Cellular Network: A Deep Reinforcement Learning-Based Approach

Muslih,

Kazmi,

Mazzara

et al. 2024

IEEE Access

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Caching content at base stations has proven effective at reducing transmission delays. This paper investigates the caching problem in a network of highly dynamic cache-enabled Unmanned Aerial Vehicles (UAVs), which serve ground users as aerial base stations. In this scenario, UAVs share their caches to minimize total transmission delays for requested content while simultaneously adjusting their locations. To address this challenge, we formulate a non-convex optimization problem that jointly controls UAV mobility, user association, and content caching to minimize transmission delay time. Considering the highly dynamic environment where traditional optimization approaches fall short, we propose a deep reinforcement learning (RL)-based algorithm. Specifically, we employ the actor-critic-based Deep Deterministic Policy Gradient (DDPG) algorithm to solve the optimization problem effectively. We conducted extensive simulations with respect to different cache sizes and the number of associated users with their home UAVs and compared our proposed algorithm with two baselines. Our proposed solution has demonstrated noteworthy enhancements over the two baseline approaches across various scenarios, including diverse cache sizes and varying numbers of users associated with their respective home UAVs.

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Deep Reinforcement Learning for Caching Placement and Content Delivery in UAV NOMA Networks

Cited by 10 publications

References 10 publications

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

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

A Survey on Cache-Aided NOMA for 6G Networks

Cache Sharing in UAV-Enabled Cellular Network: A Deep Reinforcement Learning-Based Approach

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