Soft Sensing-Based Multiple Access for Cognitive Radio Networks

We consider the effects of spectrum sensing errors on the performance of cognitive radio networks from a queueing theory point of view. In order to alleviate the negative effects of those errors, a novel design of spectrum access mechanism is proposed. This design is based on the observation that, in a binary hypothesis testing problem, the value of the test statistic can be used as a confidence measure for the test outcome. This value is hence used to specify a channel access probability for the secondary network. The access probabilities as a function of the sensing metric are obtained via solving an optimization problem designed to maximize the secondary service rate given a constraint on primary queue stability. The problem is shown to be convex and, hence, the global optimum can be obtained efficiently. Numerical results reveal a significant performance improvement in the maximum stable throughput of both primary and secondary networks over the conventional technique of making a hard binary decision and then transmitting with a certain probability if the primary is sensed to be inactive.