Abstract
The work behind research is energy and time efficient spectrum occupancy analysis for the deployment of cognitive radio (CR) in India. The measurement campaign is initial step, which provides data for analysis. The campaigns conducted worldwide lead to a complex measurement set up. So, first aim was to provide solution for simple and compact measurement set up. A new wideband circularly polarized microstrip antenna is proposed instead of existing commercially available antennas; which show better spectrum sensing ability. The conventional spectrum occupancy analysis consumes more sensing time as well as network resources because it senses whole spectrum every time. This problem can be overcome by adopting predictive method to predict the behavior of spectrum. A new hybrid neural network (HNN) model is proposed as a predictor for spectrum behavior sensing through which the status of different channels can be predicted by proper learning. The required database for training and validation of HNN predictor was collected from the measurement campaign conducted first time for seven days at Solapur city, India. The HNN model performance was examined for popular bands, and different days (weekdays and weekend) using root mean square error (RMSE) performance metric. The result shows, it has greater ability as spectrum behavior sensing predictor, and would be better choice for energy as well as time efficient spectrum occupancy analysis for cognitive radio application.
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Shirgan, S.S., Bombale, U.L. Hybrid Neural Network Based Wideband Spectrum Behavior Sensing Predictor for Cognitive Radio Application. Sens Imaging 21, 27 (2020). https://doi.org/10.1007/s11220-020-00293-4
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DOI: https://doi.org/10.1007/s11220-020-00293-4