Living on the edge: The role of proactive caching in 5G wireless networks

By Ejder Bastug, Medhi Bennis, Merouane Debbah. IEEE Communications Magazine, 2014

1,132 citations recorded by the source corpus.

This article explores one of the key enablers of beyond 4G wireless networks leveraging small cell network deployments, proactive caching. Endowed with predictive capabilities and harnessing recent developments in storage, context awareness, and social networks, peak traffic demands can be substantially reduced by proactively serving predictable user demands via caching at base stations and users' devices. In order to show the effectiveness of proactive caching, we examine two case studies that exploit the spatial and social structure of the network, where proactive caching plays a crucial role. First, in order to alleviate backhaul congestion, we propose a mechanism whereby files are proactively cached during off-peak periods based on file popularity and correlations among user and file patterns. Second, leveraging social networks and D2D communications, we propose a procedure that exploits the social structure of the network by predicting the set of influential users to (proactively) cache strategic contents and disseminate them to their social ties via D2D communications. Exploiting this proactive caching paradigm, numerical results show that important gains can be obtained for each case study, with backhaul savings and a higher ratio of satisfied users of up to 22 and 26 percent, respectively. Higher gains can be further obtained by increasing the storage capability at the network edge.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/cef2d4c27f8ff2583dc0f049a5bb93d6bf6a0edd

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In Computer Networks and Communications

Citations in the corpus

Design of Contract-Based Trading Mechanism for a Small-Cell Caching System (2017, background): “Unfortunately, the capacity of current networks has not been able to keep a similar pace with the tremendous growth of cellular traffic [1]–[3].”
Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions (2019, background): “Researchers have obtained significant improvements in the context of edge caching just by applying off-the-shelf ML algorithms, such as k-means clustering and non-parametric Bayesian learning [64].”
A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks (2017, methodology): “In SON, K-means can be found in mobility optimization [60], caching problems [82], resource optimization [56], [88], fault detection [108], and cell outage management [103].”
5G-Enabled Tactile Internet (2016, background): “Worth highlighting is the recent work [74] which advocates for proactive caching by using advanced predictive process capabilities as well as the massively improved cloud technologies discussed before.”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, background): “Such predictions lie at the heart of the notion of proactive wireless networking [162], in which a wireless network can predict its users’ behavior and adapt its operation to such behavior.”
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications (2017, background): “At last, we discuss the advantages of mobile edge networks.”
Ultra-dense edge caching under spatio-temporal demand and network dynamics (2017, background): “In this respect, edge caching is a promising UDN enabler that alleviates backhaul congestion by storing popular content during off-peak hours at the network edges, i.e. SBSs [3]– [5], yielding to an ultra-dense edge caching network (UDCN).”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “, wireless edge caching, stands out as a promising solution for efficient content delivery [32], [33], [34], [35].”
5G D2D Networks: Techniques, Challenges, and Future Prospects (2018, methodology): “A popular content caching technique is also discussed in [62], where a popularity matrix is formulated.”
A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms (2019, background): “In Reference [18], the authors propose to cache proactively the popular files at both base stations and end devices during off-peak period and to leverage the correlations among social networks and D2D communication links to reduce the peak traffic demands.”
Content-Centric Sparse Multicast Beamforming for Cache-Enabled Cloud RAN (2015, background): “Caching, on the other hand, brings contents closer to users by pre-fetching the contents during the off-peak time and hence can greatly reduce the network congestion and improve the user-perceived experience [5], [8], [9].”
Modeling and Analysis of Cellular Networks Using Stochastic Geometry: A Tutorial (2016, background): “For instance, it is advised to transport and cache popular files in the cellular network edge during off-peak time to maximize the utilization of the core network and enhance the end user quality of service [178].”
Proactive edge computing in latency-constrained fog networks (2017, background): “While the idea of proactive networks has been recently studied in the context of wireless content caching, such as [10] and [11], none of these works investigate the problem of proactive caching of computing tasks.”
Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges (2018, background): “In addition, followed by proactive computing [26], [102], the results of popular tasks can also be cached in advanced.”
Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges (2016, background): “There has been a vast body of works concentrated on development of advanced mobile content caching and delivery techniques (see e.g. [13] and references therein); however, these works rarely exploit the synergy of caching and computing at the cache nodes.”