Joint Content Popularity Prediction and Content Delivery Policy for Cache-Enabled D2d Networks: A Deep Reinforcement Learning Approach

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

doi:10.1109/globalsip.2018.8646427

Cited by 13 publications

(7 citation statements)

References 11 publications

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“…The work in [136] focuses on content placement and delivery strategies in cache-enabled D2D networks, aiming at minimizing the content delivery delay and the power consumption. ESN-based learning is employed for predicting the content popularity and user mobility patterns, determining what and where to cache.…”

Section: ) Energy Efficiencymentioning

confidence: 99%

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A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

et al. 2022

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Mobile networks experience a tremendous increase in data volume and user density due to the massive number of coexisting users and devices. An efficient technique to alleviate this issue is to bring the data closer to the users by exploiting cache-aided edge nodes, such as fixed and mobile access points, and even user devices. Meanwhile, the fusion of machine learning and wireless networks offers new opportunities for network optimization when traditional optimization approaches fail or incur high complexity. Among the various machine learning categories, reinforcement learning provides autonomous operation without relying on large sets of historical data for training. In this survey, reinforcement learning-aided mobile edge caching solutions are presented and classified, based on the networking architecture and optimization target. As sixth generation (6G) networks will be characterized by high heterogeneity, fixed cellular, fog, cooperative, vehicular, and aerial networks are studied. The discussion of these works reveals that there exist reinforcement learning-aided caching schemes with varying complexity that can surpass the performance of conventional policy-based approaches. Finally, several open issues are presented, stimulating further interest in this important research field.INDEX TERMS 6G, edge caching, heterogeneous networks, machine learning, mobile edge networks, reinforcement learning.

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Section: ) Energy Efficiencymentioning

confidence: 99%

“…Apart from [136], jointly tackling energy and delay performance concerns through two-step learning, i.e., ESN-based prediction and DQN-based content delivery, there have been various RL strategies focusing on delay reduction.…”

Section: ) Delay Reductionmentioning

confidence: 99%

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

et al. 2022

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“…ML techniques can learn where to cache by predicting user locations from location histories and current context [31]. Similarly, ML techniques can optimize the caching decisions while contemplating network state and constraints of D2D and mmWave communication [53], [42]. Moreover, ML techniques can learn time-series content popularity from user request patterns and content features [54].…”

Section: A Motivationmentioning

confidence: 99%

“…The initial user mobility is adopted from pedestrian mobility patterns according to which a user visits places of interest at a specific time of the day. Researchers [53] use user location history as input to ESN to predict mobility patterns and user context as input to another ESN to predict content popularity.…”

Section: Techniquesmentioning

confidence: 99%

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Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

Shuja,

Bilal,

Alasmary

et al. 2020

Preprint

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Edge networks provide access to a group of proximate users who may have similar content interests. Caching popular content at the edge networks leads to lower latencies while reducing the load on backhaul and core networks with the emergence of high-speed 5G networks. Edge networks are complex and dynamic. User mobility, preferences, and content popularity are the dominant dynamic features of the edge networks. Temporal and social features of content, such as the number of views and likes are applied to estimate the popularity of content from a global perspective. However, such estimates may not be mapped to an edge network with particular social and geographic characteristics. In edge networks, machine learning techniques can be applied to predict content popularity based on user preferences, user mobility based on user location history, cluster users based on similar content interests, and optimize cache placement and replacement strategies provided a set of constraints and predictions about the state of the network. These applications of machine learning can help identify relevant content for an edge network to lower latencies and increase cache hits. This article surveys the application of machine learning techniques for caching content in edge networks. We survey recent state-of-the-art literature and formulate a comprehensive taxonomy based on (a) machine learning technique (application and objective), (b) caching strategy (policy, location, and objective), and edge network (type and delivery strategy). We further survey supporting concepts for optimal edge caching decisions that require the application of machine learning. These supporting concepts are social-awareness, popularity prediction, and community detection in edge networks. A comparative analysis of the state-of-the-art literature is presented with respect to the parameters identified in the taxonomy. Moreover, we debate research challenges and future directions for optimal caching decisions and the application of machine learning towards caching in edge networks.

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AI Models for Green Communications Towards 6G

Mao

Tang

Kawamoto

et al. 2022

IEEE Commun. Surv. Tutorials

166

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Joint Content Popularity Prediction and Content Delivery Policy for Cache-Enabled D2d Networks: A Deep Reinforcement Learning Approach

Cited by 13 publications

References 11 publications

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

AI Models for Green Communications Towards 6G

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