The effect of additional statistical side information on multiple antenna spectrum sensing

In this paper, we consider the problem of multiple antenna spectrum sensing in Cognitive Radios (CR) when some or all parameters are unknown. The Generalized Likelihood Ratio (GLR) test is the convectional method to solve the composite hypothesis testing problem in which the detection and estimation sub-problems are considered separately. In this paper, the multiple spectrum sensing problem is solved using a novel approach in which the the detection and estimation sub-problems considered jointly and the resulted detectors are optimal under finite number of samples. We assume some additional side statistical information is available for unknown parameters and as theoretical results of the novel GLR detector imply, the optimal way of using this additional side information, is to use them in the Maximum A-Posteriori (MAP) or Minimum Mean Square Error (MMSE) estimation of unknown parameters for constructing the GLR tests. The simulation results show that the newly derived GLR detectors outperform traditional GLR detectors significantly. Also for the situation that all parameters are unknown the proposed detectors are compared with the Energy Detector (ED), where the simulation results indicate that the proposed detectors not only have significantly better performance, but also are robust to practical noise mismatch.