To scan or not to scan: The effect of channel heterogeneity on optimal scanning policies

Cognitive Networks have been proposed to opportunistically discover and exploit (temporarily) unused licensed spectrum bands. For a number of applications, high throughput is the key figure of merit, while the application is still elastic enough to be supported at different rates. To this end, the cognitive node will try to discover and pool together a number of (at the time available) primary channels to provide a given target throughput. When a single radio is used for both transmission and channel scanning, an interesting tradeoff arises: when one or more channels of the currently available ones are lost (e.g. primary user returns), should the node start scanning immediately or continue transmitting over the remaining channels. Using renewal-reward theory, we show that if the goal is to maximize the average (long-term) throughput, the answer to this question depends on the statistics of the channel availability periods. Specifically, for relatively homogeneous channels, we show that it is optimal to start scanning immediately, while for heterogeneous channels, it is often better to defer scanning, even if multiple channels are lost. Simulations for a range of different channel characteristics validate our analytical findings and suggest that triggering the scanning function at the right times, can improve performance considerably.