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Top1. Introduction
The recent decade has witnessed rapid proliferation of wireless technologies and their significant impact on all aspects of our lives. However, these disruptive wireless technologies exhaust the limited radio spectrum, which is referred to as spectrum scarcity (Wang et al., 2011; Peha, 2009). This issue leaves little or no spectrum for future demands. Spectrum scarcity has become increasingly serious leading to intensified attention. Cognitive radio network (CRN) is commonly envisioned as a promising solution to relieve spectrum scarcity and significantly improve spectrum efficiency. In recent years, CRNs have been gaining considerable attention and related research on a variety of topics can be found in the literature (Gao et al., 2004; Qiu et al., 2012; Liu et al., 2008; Zhao et al., 2011; Lee et al., 2012).
A typical CRN is comprised of two types of users: primary users (PUs) and secondary users (SUs). PUs are authorized to utilize licensed bands/channels whenever they have demands. In contrast, SUs are not licensed users, but they are allowed to temporarily access channels without harmful interference to the PUs. If the interference from SUs to PUs is dominant and destructive, SUs have to take necessary actions to avoid it. For instance, SUs may need to withdraw from the channel immediately or reduce their transmission power. This distinct feature of CRNs raises an essential and challenging question, i.e., how to accurately estimate or predict interference from SUs to PUs. This topic has recently attracted considerable attention (e.g., Hong et al., 2008; Chen et al., 2010; Rabbachin et al., 2011). Most of the existing models and related analysis are significantly complex.
In this paper, our goal is to estimate the total interference from SUs at any PU receiver by developing a considerably simpler probabilistic interference model in which spectrum sensing is taken into account. Spectrum sensing plays a critical and fundamental role in CRNs. SUs have to continually monitor the channel before their transmission. The two-state sensing model is commonly used in the process of spectrum sensing, which classifies a channel into either busy or idle state (Zhao et al., 2012). Secondary users can only be allowed to utilize a channel when it is detected as idle.
With the two-state sensing model, the SUs that detect an active user, either a PU or another SU in their sensing ranges are prohibited from transmitting. As a result, it is interesting to observe that the maximum number of simultaneously interfering SUs is finite, typically ranging from 1 to 4 (Zhao et al., 2013). We are inspired by this significant conclusion and propose a simple probabilistic interference model for CRNs. All cases with simultaneously interfering SUs to a PU receiver are analyzed. Furthermore, in each case, the interference to primary users is derived and the corresponding probability is thoroughly investigated as well. This promising and effective approach is expected to shed light on interference modeling in CRNs with spectrum sensing.
The rest of the paper is organized as follows. In Section 2, we briefly introduce related work. Section 3 presents the network model along with assumptions. In Section 4, we introduce the proposed interference modeling along with in-depth mathematical analysis. The simulation results and discussions are presented in Section 5. Concluding remarks are stated in Section 6.