Adaptive Scheduling of Spectrum Sensing Periods in Cognitive Radio Networks

We consider a cognitive radio (CR) network that makes opportunistic use of a set of channels licensed to some primary network. During its operation, the CR network is required to carry out spectrum sensing to detect active primary users, thereby avoiding interfering with them. However, spectrum sensing can cause negative effects to the performance of the CR network, as all CR communication has to be postponed during channel sensing. This paper focuses on adaptively scheduling the spectrum sensing periods so that negative impacts to the performance of the CR network are minimized. We first consider the case when CR nodes always have data to transmit and experience time- varying channels. Based on knowledge of channel conditions, the sensing periods are adaptively scheduled to maximize the spectrum efficiency of the CR operation. We then consider the case when CR nodes experience both stochastic data arrival and time-varying channels. By treating each sensing period as a '
virtual

sensing

packet
', we convert the problem of joint data-transmission/spectrum-sensing scheduling into a standard queueing model. Based on that, efficient scheduling algorithms that take into account channel and queueing conditions of the CR network to achieve good quality of service are proposed.