  This paper proposes a spectrum sensing algorithm from one bit measurements in
a cognitive radio sensor network. A likelihood ratio test (LRT) for the one bit
spectrum sensing problem is derived. Different from the one bit spectrum
sensing research work in the literature, the signal is assumed to be a discrete
random correlated Gaussian process, where the correlation is only available
within immediate successive samples of the received signal. The employed model
facilitates the design of a powerful detection criteria with measurable
analytical performance. One bit spectrum sensing criterion is derived for one
sensor which is then generalized to multiple sensors. Performance of the
detector is analyzed by obtaining closed-form formulas for the probability of
false alarm and the probability of detection. Simulation results corroborate
the theoretical findings and confirm the efficacy of the proposed detector in
the context of highly correlated signals and large number of sensors.
