Learning methodologies for wireless big data networks: A Markovian game-theoretic perspective

Yang, Chungang

doi:10.1016/j.neucom.2015.04.111

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…The authors in Ref. [19] presented a multiple cognitive agent-based divide-and-conquer network management and control architecture and proposed a Markovian gametheoretic modeling framework. In addition, they fo-cused on the construction of state space, the state transition computation, and the convergence of the parallel Q-learning technique, which provides a suitable and effective modeling tool, as well as various learning techniques for wireless big data networks.…”

Section: Data Analyticsmentioning

confidence: 99%

Survey of wireless big data

Qian

Zhu

Zhang

2017

J. Commun. Inf. Netw.

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Wireless big data describes a wide range of massive data that is generated, collected and stored in wireless networks by wireless devices and users. While these data share some common properties with traditional big data, they have their own unique characteristics and provide numerous advantages for academic research and practical applications. This article reviews the recent advances and trends in the field of wireless big data. Due to space constraints, this survey is not intended to cover all aspects in this field, but to focus on the data aided transmission, data driven network optimization and novel applications. It is expected that the survey will help the readers to understand this exciting and emerging research field better. Moreover, open issues and promising future directions are also identified.

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Section: Data Analyticsmentioning

confidence: 99%