Accurate and Practical Energy Detection over xcexb1xcexbc Fading Channels In this study, a novel and exact closedform expression for detection probability of energy detection (ED) in terms of Meijerxe2x80x99s Gfunction over xcexb1xcexbc generalized fading channels was derived. It is more accurate and practical than the existing exact expressions and has wide application prospects in the performance evaluations in various areas of wireless communications, especially in the wireless sensor network (WSN) and the cognitive radio network (CRN). Furthermore, an exact and simple analytical solution for the sample size meeting the desired detection performance in terms of the probability mass function of a Poisson distribution was also solved. Simulations verified the detection performance and accuracy of our derived expressions with a small sample size compared to the existing exact expressions and approximations.

A TensorBased Spectrum Sensing Technique for MIMO Cognitive Radio Networks Despite the numerous spectrum sensing techniques, the existing techniques fail in providing an efficient spectrum sensing whenever a hidden terminal problem arises. Meanwhile, this problem can happen at any time in any severely fading primarytosecondary channels resulting in very low primary signaltonoise ratios (SNRs) and hence ineffective detection of the primary user in a cognitive radio (CR). Towards overcoming this problem, by introducing a tensorbased hypothesis testing framework, this paper proposes an efficient tensorbased detector (TBD) for a multipleinput multipleoutput (MIMO) CR networks over multipath fading channels. MonteCarlo simulations demonstrate that TBD outperforms the generalized likelihood ratio test (GLRT) detector and maximumminimum eigenvalue (MME) detector, especially in the very low SNR regime which is a manifestation of the hidden terminal problem.