Received signal interpolation for context discovery in cognitive radio

This paper addresses the context acquisition to characterize different features of a primary network based on the power measurements obtained by a sensor network. In particular, it presents a comparative study of the impact of natural neighbor, linear, and nearest neighbor interpolation techniques carried out over the measurements at different geographical positions. Extracted features include transmitter position, antenna pattern or propagation model. An evaluation is carried out in scenarios including the effect of both correlated and non-correlated shadowing.Peer ReviewedPostprint (author’s final draft

Spectrum cartography techniques, challenges, opportunities, and applications: A survey

The spectrum cartography finds applications in several areas such as cognitive radios, spectrum aware communications, machine-type communications, Internet of Things, connected vehicles, wireless sensor networks, and radio frequency management systems, etc. This paper presents a survey on state-of-the-art of spectrum cartography techniques for the construction of various radio environment maps (REMs). Following a brief overview on spectrum cartography, various techniques considered to construct the REMs such as channel gain map, power spectral density map, power map, spectrum map, power propagation map, radio frequency map, and interference map are reviewed. In this paper, we compare the performance of the different spectrum cartography methods in terms of mean absolute error, mean square error, normalized mean square error, and root mean square error. The information presented in this paper aims to serve as a practical reference guide for various spectrum cartography methods for constructing different REMs. Finally, some of the open issues and challenges for future research and development are discussed.publishedVersio

Received signal interpolation for context discovery in cognitive radio

This paper addresses the context acquisition to characterize different features of a primary network based on the power measurements obtained by a sensor network. In particular, it presents a comparative study of the impact of natural neighbor, linear, and nearest neighbor interpolation techniques carried out over the measurements at different geographical positions. Extracted features include transmitter position, antenna pattern or propagation model. An evaluation is carried out in scenarios including the effect of both correlated and non-correlated shadowing.Peer Reviewe

Received signal interpolation for context discovery in cognitive radio

This paper addresses the context acquisition to characterize different features of a primary network based on the power measurements obtained by a sensor network. In particular, it presents a comparative study of the impact of natural neighbor, linear, and nearest neighbor interpolation techniques carried out over the measurements at different geographical positions. Extracted features include transmitter position, antenna pattern or propagation model. An evaluation is carried out in scenarios including the effect of both correlated and non-correlated shadowing.Peer Reviewe