Wireless caching: technical misconceptions and business barriers

By G. Paschos, Ejder Bastug, Ingmar Land, Giuseppe Caire, Merouane Debbah. IEEE Communications Magazine, 2016

310 citations recorded by the source corpus.

Caching is a hot research topic and poised to develop into a key technology for the upcoming 5G wireless networks. However, the successful implementation of caching techniques crucially depends on joint research developments in different scientific domains such as networking, information theory, machine learning, and wireless communications. Moreover, there are business barriers related to the complex interactions between the involved stakeholders: users, cellular operators, and Internet content providers. In this article we discuss several technical misconceptions with the aim of uncovering enabling research directions for caching in wireless systems. Ultimately, we make a speculative stakeholder analysis for wireless caching in 5G.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/6a61aa6338541fee93c8ca44ec45c6fb2b350c48

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In Computer Networks and Communications

Citations in the corpus

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications (2017, background): “2) Dynamic Model: The static IRM model cannot reflect the real content popularity which is time varying [91].”
Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities (2017, methodology): “To this end, machine learning tools can provide 5G cellular networks with efficient caching, in which a “smart” caching control unit (CCU) can learn, track, and possibly adapt to the space-time popularities of reusable contents [2], [5].”
Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions (2019, methodology): “, video streams) during off-peak hours, storing the data in the SBs’ memory units, and reusing them during peak traffic periods [135], [136].”
Big data caching for networking: moving from cloud to edge (2016, background): “Telcos are looking for decentralized and flexible network architectures where predictive resource management plays a crucial role, thanks to the recent advances in storage/memory, context-awareness and edge/cloud computing [6]–[9].”
A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms (2019, methodology): “In the independence reference model, contents are requested according to an independent Poisson process with the rate corresponding to the content popularity [112].”
A Very Brief Introduction to Machine Learning With Applications to Communication Systems (2018, background): “, for caching popular contents, may not benefit from well-established and reliable models, making a data-driven approach desirable (see [26], [27]).”
Toward Interconnected Virtual Reality: Opportunities, Challenges, and Enablers (2017, methodology): “In the numerical setups of the following case studies, the arrival process of AR/VR devices shall follow a Shot Noise Model [10] with a total time period of Tmax hours.”