Treffer: A Big Data architecture for spectrum monitoring in cognitive radio applications.
Weitere Informationen
Cognitive radio has emerged as a promising candidate solution to improve spectrum utilization in next-generation wireless networks. A crucial requirement for future cognitive radio networks is the wideband spectrum sensing, which allows detecting spectral opportunities across a wide frequency range. On the other hand, the Internet of Things concept has revolutionized the usage of sensors and of the relevant data. Connecting sensors to cloud computing infrastructure enables the so-called paradigm of Sensing as a Service (S<sup>2</sup>aaS). In this paper, we present an S<sup>2</sup>aaS architecture to offer the Spectrum Sensing as a Service (S<sup>3</sup>aaS), by exploiting the flexibility of software-defined radio. We believe that S<sup>3</sup>aaS is a crucial step to simplify the implementation of spectrum sensing in cognitive radio. We illustrate the system components for the S<sup>3</sup>aaS, highlighting the system design choices, especially for the management and processing of the large amount of data coming from the spectrum sensors. We analyze the connectivity requirements between the sensors and the processing platform, and evaluate the trade-offs between required bandwidth and target service delay. Finally, we show the implementation of a proof-of-concept prototype, used for assessing the effectiveness of the whole system in operation with respect to a legacy processing architecture. [ABSTRACT FROM AUTHOR]
Copyright of Annals of Telecommunications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)