Interference Modeling Based On Cognitive Radio Network

The rapid development of communication services makes the problem of the shortage of spectrum resources more and more serious. Cognitive radio technology has made improvements on the existing spectrum using mode, Making use of spectrum more flexible and efficient. However, a key problem to restrict the application of cognitive radio is how to make the primary users and secondary users in cognitive radio networks coexist harmoniously. Because of the immaturity of the sensing technology in cognitive radio network, the second users will certainly cause interference to the primary users. Therefore, it is very important to establish the interference model and analyze, control, eliminate the interference based on the interference model. At present, the research of interference modeling is still at a preliminary stage in domestic and foreign countries, especially in domestic, the research is very little. There are many aspects need to consider in the interference modeling, such as the spatial layout of the nodes, the mobility of the nodes, communication modulation, the communication channel(Rayleigh distribution, Nakagami fading, shadowing and other factors). The ultimate purpose of interference modeling is analyzing the interference, which can give guidance to the practical application of the cognitive radio. Currently, there are two kinds of research about interference modeling, namely interference modeling research based on interference assessment and interference modeling research based on cognitive radio network applications.Chapter III of this paper proposed an interference model in which the second users obey the Poisson distribution. Unlike the traditional simple accumulation interference model, in this paper, we consider the probability of a second user interference and introduced this probability into the interference model. Chapter IV proposed two kinds of improvement on the basis of Chapter III. In the first improved interference model, the second users no longer exist separately, but exist in cell units. In the second improved interference model, we treat the transmitting end and the receiving end of the primary users separately. These two improvements are more consistent with the real environment and more able to reflect the real environment interference accurately.We have simulation test based on the interference model in Chapter III and the two improved models in Chapter IV and analyze communication outage probability of each interference model. The simulation results show that the interference models can reflect the real environment well. The impact of environmental factors on the communication outage probability has two sides. On the one hand the environmental factors would undermine the notification signal, which makes the probability of the second users noticing the notification signal smaller and the probability of producing interference bigger. On the other hand, it will weaken the interference to the primary user caused by the second users. What’s more, various environmental factors affect the communication outage probability more complexly when discuss the transmitter and the receiver separately. We also conducted a simulation of the different factors that affect communications outage probability. Finally, we compared the interference model in Chapter III and the two improved interference model in Chapter IV. The simulation results show that the two improved interference model in Chapter IV, compare with the interference model in Chapter III, are more consistent with the real environment and more able to reflect the real environment interference accurately.