Dynamic cooperative spectrum sensing in cognitive radio networks

Abstract

The prime objective of spectrum sensing in Cognitive Radio (CR) networks is to increase the efficiency of spectrum utilization and improve the network performance. Both of false probability and misdetection probability can reduce the network performance. We use the mean error detection probabilities to denote the network performance. As the value of the mean detection probabilities decreases, the network performance will be improved. In order to achieve this aim, we use the Markov model of the channel occupancy to estimate the prior probabilities for the hypothesis test in dynamic traffic environment. Because of the uncertainty under local detection, the spectrum detection scheme is extended to cooperative sensing among multiple second users. Moreover, we also propose a dynamic decision threshold to optimize the cooperative spectrum detection. The numerical results show that our proposed scheme can further reduce the mean error detection probabilities.

Over-the-Air Modulation Classification using Deep Learning in Fading Channels for Cognitive Radio

“…Thus, the difficulty of precisely recognizing modulation schemes becomes more difficult. The challenge of CR is to identify and differentiate (4) various radio signals which has different demands and behaviors under fading channels that may occur due to user mobility. CR systems face the challenge of detecting a radio signal's modulation type in order to determine what type of communication scheme and transmitter is there.…”