When to quit for a new job: Quickest detection of spectrum opportunities in multiple channels

We consider quickest detection of idle/off periods in multiple on-off processes. We show that this problem presents a fresh twist to the classic signal processing problem of quickest change detection that considers only one stochastic process. In particular, we demonstrate that the key to quickest change detection in multiple processes is to abandon the current process when its state is unlikely to change in the near future (as indicated by the measurements obtained so far) and seek opportunities in a new process. This problem arises in spectrum opportunity detection in cognitive radio networks where a secondary user searches for idle channels in the spectrum. A Bayesian formulation of quickest change detection in multiple on-off processes with geometrically distributed busy and idle times is obtained within a decision-theoretic framework. Based on the structure of the resulting sequentially decision problem, we propose a low-complexity threshold policy for channel switching and change detection and demonstrate its superior performance over the single-channel approach.