A strategy for classifying a set of dissimilar channels by their a priori channel occupancy probability

Recent Changes in policy regarding the opportunistic use of licensed radio spectrum have paved the way for new innovative technologies like cognitive radio (CR). In CR systems a secondary user (SU) is allowed to use open channels if the primary user (PU) is not currently using them. Regulatory bodies like the FCC establish maximum interference requirements for SUs when making use of these channels. To comply with these requirements SUs must measure the occupancy of each of the channels they intend to use. Any strategy employed for opportunistic spectrum usage has to consider the tradeoffs between time spent searching for empty channels and time spent using those empty channels. In most cases the spectrum sensing that is employed by a CR system starts with no prior information about the occupancy of the channels it intends to use. We propose a novel method of addressing this lack of prior knowledge by employing an efficient strategy that classifies some of the channels the SU intends to use within a fixed time limit. This classifier can be run before the SUs attempt transmission, and will provide the SUs' spectrum sensing sub-systems with a set of occupancy probabilities for some of the channels. Our classifier will be designed around sequential probability ratio tests (SPRT) because these tests maintain bounds on classification errors while using the smallest number of samples for classification. The classifier will attempt to classify as many channels as possible within the given time limit by intelligently allocating channel measurements. We will examine the system's performance in various measurement resource regimes and identify regimes where our approach is superior to simpler classification schemes.M.S.Includes bibliographical referencesby Shridatt Sugri