Spectrum monitoring is important for efficient spectrum sharing and resource management in cloud-based radio access networks (C-RAN). In this paper we show how data obtained from long-term spectrum monitoring together with machine learning (ML) operating on big data (BD) can be used in a C-RAN scenario for spectrum management purposes. We propose an approach for spectrum occupancy forecasting which can be used to reduce the delay in making dynamic spectrum allocation decisions and improve the cognitive and management functionalities of cloud-based architectures such as C-RAN. The spectrum occupancy and usage activity in a predefined frequency band is based on the statistical processing of a large amount of collected data and the introduction of a frequency-time resources indicator as a measure of spectrum usage. Furthermore, we apply ML algorithms to predict spectrum usage and compare the predicted with actual measured data. Taking into consideration that the accuracy of the prediction depends on the volume of collected data and the time of prediction on the BD and ML approach, we propose the development of a cloud-based generic processing architecture to solve the ''accuracy versus latency'' trade-off problem. The proposed architecture is appropriate for deployment in cognitive C-RAN.

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This paper researches a novel 2D Z-shaped Electromagnetic Band-Gap (EBG) structure, its dispersion diagram and application field. Based on a transmission line model, the dispersion equation is derived and theoretically investigated. In order to validate theoretical results, a full wave analysis is performed and the electromagnetic properties of the structure are revealed. The theoretical results show good agreement with the full wave simulation results. The frequency response of the structure is compared to the well know structures of Jerusalem cross and patch EBG. The results show the applicability of the proposed 2D Z-shaped EBG in microstrip patch antennas, microstrip filters and high speed switching circuits, where the suppression of parasitic surface wave is required.