Deep Reinforcement Learning Approaches for Content Caching in Cache-Enabled D2D Networks

Li, Lixin; Yang, Xu; Yin, Jiaying; Liang, Wei; Li, Xu; Chen, Wei; Han, Zhu

doi:10.1109/jiot.2019.2951509

Cited by 109 publications

(75 citation statements)

References 35 publications

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“…In [161], neural network and DRL technique were utilized to design energy-aware optimum content allocation and forwarding for a network with D2D caching capability. The authors in [162] optimized transmission zone for content delivery and minimized energy consumption.…”

Section: B Green Caching Techniques For Content Placementmentioning

confidence: 99%

“…Recurrent neural network (RNN) and DRL [161]. Lagrange multipliers method and Mode selection algorithm [162].…”

Section: D2d Networkmentioning

confidence: 99%

“…It represents the energy consumed by the whole system. In the existing studies [26], [49]- [51], [118], [119], [144], [145], [153]- [158], [161], [173], [176], [180], the authors utilized energy consumption to compare the performance gain of the proposed caching model with other systems. Wireless networks utilize a similar metric named area power consumption [115], [139].…”

Section: ) Energy Consumptionmentioning

confidence: 99%

“…Cache hit ratio (CHR) is calculated as a ratio of the number of requests served by the caching node over the total number of content requests. To measure the effectiveness of a caching system, a number of studies [36], [40], [153], [154], [161], [173], [217]. utilized this metric.…”

Section: B Caching-related Metrics 1) Cache Hit Ratiomentioning

confidence: 99%

“…Mobility issues and the ever-increasing number of smart vehicles worsen the situation. To overcome Energy consumption [13], [26], [47], [49]- [51], [118]- [120], [126], [127], [130], [132], [134], [136], [143]- [145], [147], [149]- [158], [160], [161], [173], [175], [176], [180], [181], [189], [197]- [200], [207], [211], [218].…”

Section: Vehicular Communicationmentioning

confidence: 99%

See 4 more Smart Citations

A Review on Green Caching Strategies for Next Generation Communication Networks

et al. 2020

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In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching.

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Section: B Green Caching Techniques For Content Placementmentioning

confidence: 99%

“…Recurrent neural network (RNN) and DRL [161]. Lagrange multipliers method and Mode selection algorithm [162].…”

Section: D2d Networkmentioning

confidence: 99%

Section: ) Energy Consumptionmentioning

confidence: 99%

Section: B Caching-related Metrics 1) Cache Hit Ratiomentioning

confidence: 99%

Section: Vehicular Communicationmentioning

confidence: 99%

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A Review on Green Caching Strategies for Next Generation Communication Networks

et al. 2020

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Blockchain‐Empowered Federated Learning Approach for An Intelligent and Reliable D2D Caching Scheme

Cheng,

Sun,

Liu

et al. 2023

Federated Learning for Future Intelligent Wireless Networks

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Cache-enabled device-to-device (D2D) communication is a potential approach to tackle the resource shortage problem. However, public concerns of data privacy and system security still remain, which thus arises an urgent need for a reliable caching scheme. Fortunately, federated learning (FL) with a distributed paradigm provides an effective way to privacy issue by training a high-quality global model without any raw data exchanges. Besides the privacy issue, blockchain can be further introduced into the FL framework to resist the malicious attacks occurred in D2D caching networks. In this study, we propose a double-layer blockchain-based deep reinforcement FL (BDRFL) scheme to ensure privacy-preserved and cachingefficient D2D networks. In BDRFL, a double-layer blockchain is utilized to further enhance data security. Simulation results first verify the convergence of the BDRFL-based algorithm, and then demonstrate that the download latency of the BDRFL-based caching scheme can be significantly reduced under different types of attacks when compared to some existing caching policies.

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Content sharing strategy for blind popularity distribution in D2D communications

Zhuang

Song²,

Chen

et al. 2023

Trans Emerging Tel Tech

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The development of multimedia content continuously encourages the appearances of new multimedia applications. Meanwhile, Device to Device (D2D) is regarded as an important 5G technology that creates a direct connection between two mobile devices. We combine the content sharing traits in D2D network situations in response to the problem of massive amounts of multimedia content being distributed. As a result, in this paper, we provide a popularity‐based information mining and content placement strategy for blind popularity distribution in D2D scenarios. To analyze the cache hit performance in D2D networks under the presumption of deterministic content popularity, we first construct a D2D content caching framework. Then, we design a multiarm bandits model and suggest a single and multicache placement policy based on online learning for blind popularity in D2D networks. Finally, the experimental results demonstrate that the proposed method achieves better convergence and a better cache hit ratio than the other strategies.

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Deep Reinforcement Learning Approaches for Content Caching in Cache-Enabled D2D Networks

Cited by 109 publications

References 35 publications

A Review on Green Caching Strategies for Next Generation Communication Networks

A Review on Green Caching Strategies for Next Generation Communication Networks

Blockchain‐Empowered Federated Learning Approach for An Intelligent and Reliable D2D Caching Scheme

Content sharing strategy for blind popularity distribution in D2D communications

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