Spectrum sensing for dynamic primary user with multiple transitions under Rayleigh distributed channel attenuation in additive Laplacian noise

Abstract

We propose a real-time scenario of dynamic behavior of the primary user (PU) with multiple transitions under the Rayleigh distributed channel attenuation (RDCA) for spectrum sensing. We assume Multiple access interference (MAI) as an additive noise which is modeled using Laplacian noise model. Apart from this, we consider higher order modulation scheme such as Quadrature Amplitude Modulation (QAM) with its modulation order M. The Markov chain models the transitions of the PU using transition probabilities (TPs) which are functions of the Markov Chain parameters (MCPs) ψ and ξ. The MCPs are assumed to be known at the receiver. In improved absolute value cumulation detection (i-AVCD) scheme, decision statistic is obtained when the absolute value of received observation at the cognitive radio (CR) is raised to an exponent P with P∈(0,2]. The AVCD and ED (energy detection) are special cases of i-AVCD at P=1 and P=2, respectively. The TPs are used with i-AVCD to get a modified detection scheme, referred to as Mr: i-AVCD. The Mr refers to the term ‘modified’ under the effect of MCPs. Special cases, referred to as Mr: AVCD and Mr: ED are formed for the Mr: i-AVCD scheme at P=1 and P=2, respectively. Further, we consider the case of a multiple antenna system at the receiver. We derive closed-form expressions of the detection probability (PD) and false alarm probability (PF). Finally, we present the results using receiver operating characteristics (ROC) and PD vs. average SNR (γ). Our analytical findings are corroborated using a close match between simulations and analytical results. Besides this, the modified schemes have been compared with the conventional schemes. In conventional schemes, no information on MCPs is available at the receiver a priori. We find that the modified detection schemes (MDS) outperform conventional schemes. Further, we observe that performance of MDS degrades as we increase the value of M. We also observe that the spatial diversity improves the performance of the MDS.