Performance Analysis of Selective Opportunistic Spectrum Access With Traffic Prediction

In cognitive radio (CR) networks, the ability to capture a frequency slot for transmission in an idle channel has a significant impact on the spectrum efficiency and quality of service (QoS) of a secondary user (SU). The radio frequency (RF) front-ends of an SU have limited bandwidth for spectrum sensing with the target frequency bands dispersed in a discontinuous manner. This results in the SU having to sense multiple target frequency bands in a short period of time before selecting an appropriate idle channel for transmission. This paper addresses this technical challenge by proposing a selective opportunistic spectrum access (SOSA) scheme. With the aid of statistical data and traffic prediction techniques, our SOSA scheme can estimate the probability of a channel appearing idle based on the statistics and choose the best spectrum-sensing order to maximize spectrum efficiency and maintain an SU´s connection. By means of doing so, this SOSA scheme can preserve the QoS of an SU while improving the system efficiency. In contrast to previous work, we consider the practical issues encountered by an SU in a wireless environment, such as discontinuous target frequency bands and limited spectrum-sensing ability. We examine the spectrum-sensing scheme in terms of packet loss ratio (PLR) and throughput. The simulation results show that the proposed SOSA scheme can decrease the probability of packet losses in the discontinuous spectrum environment and improve the spectrum efficiency.