Performance Evaluation of NP and ML Based Spectrum Sensing in FDCRNs under Small Scale Fading Scenario

Full duplex (FD) cognitive radio (CR) is one of the key enabling technologies for future wireless communication systems. Improvement in the spectrum efficiency, as promised by the CR technology can be further extended to FD-CR networks (FDCRNs) by performing a highly efficient and methodical spectrum sensing. In this paper, energy detection based effective spectrum sensing in FDCRNs considering residual SI under small scale Rayleigh slow fading scenario asserting a more realistic scenario for various wireless applications is proposed. First, Neyman Pearson (NP) based energy detection of the primary user (PU) signal considering residual SI is performed and closed form analytical expressions of performance metrics (detection probability (P
_d
), and false alarm probability (P
_fa
)) are obtained as well. Further, the detection performance using NP approach is compared to the maximum likelihood (ML) based energy detection technique performed under the same aforementioned scenario. The performance evaluation through simulations shows that, the NP based energy detection technique outperforms the ML based spectrum sensing technique and also the simulated results obtained using NP approach are found in good agreement with the analytical results assuring that the analysis is carried out with more realistic assumptions.