Spectrum sensing via event-triggered sampling

Abstract

We propose a new framework for cooperative spectrum sensing in cognitive radio networks, that is based on a novel class of non-uniform samplers called the event-triggered samplers, and sequential detection. In the proposed scheme, each secondary user computes its local sensing decision statistic based on its own channel output; and whenever such decision statistic crosses certain predefined threshold values, the secondary user sends one bit (or several bits) information to the fusion center. The fusion center asynchronously receives the bits from different secondary users, based on which it updates the global sensing decision statistic and performs the sequential probability ratio test (SPRT), until reaching a sensing decision. We provide an asymptotic analysis for the above scheme, and under different conditions, we compare it against the cooperative sensing scheme that is based on traditional uniform sampling and sequential detection. Through simulations we show show that the proposed scheme, using even 1 bit, can outperform its uniform sampling counterpart that uses infinite number of bits.