Spectrum Opportunity Detection: How Good Is Listen-before-Talk?

We consider spectrum opportunity detection in cognitive radio networks for spectrum overlay. We highlight the differences between detecting primary signals and detecting spectrum opportunities. We show that besides noise and fading, the geographic distribution and traffic pattern of primary users have significant impact on the performance of spectrum opportunity detection. A necessary and sufficient condition for the equivalence between primary signal detection and spectrum opportunity detection is obtained, and the performance of listen-before- talk in a Poisson primary network with uniform traffic pattern is analyzed. Furthermore, we study the translation from the physical layer opportunity detection performance to the MAC layer performance. This issue is crucial in examining the impact of sensing errors on the design of higher layers and in choosing the optimal operating characteristics of the spectrum sensor. We demonstrate the complex dependency of the relationship between PHY and MAC on the applications and the use of MAC handshaking signaling such as RTS/CTS.