Adaptive Threshold-Based RF Spectrum Scanning Through Joint Energy and Bandwidth Detection with USRPs in Cognitive Sensor Networks for ROAR Architecture

Opportunistic spectrum access in cognitive radio networks is regarded as an emerging technology for efficient utilization of under utilized of idle radio frequency spectrum. For opportunistic spectrum access, wireless devices are required to identify idle spectrum through spectrum sensing. The performance study of existing spectrum sensing algorithms often overlooks bandwidth of the detected signal while detecting the signal using peak of the energy spectrum that crosses the pre-specified threshold. This results in high false alarm probability. In this paper, we evaluate an adaptive threshold based RF spectrum sensing approach using USRP Software Defined Radio (SDR) for real-time opportunistic spectrum access in cloud based cognitive radio networks (ROAR) architecture where both signal energy and band-width of the signal are taken into account. We evaluate the performance of the proposed approach using probability of misdetection and false alarms metrics. The proposed approach can be particularized to a scenario with energy based detection or bandwidth based detection. The proposed approach is illustrated through numerical results obtained from experiments.