Research On The Effective Channel Capacity In Cognitive Radio

In the era, which information transmission is increasingly frequent, spectrum resources become more and more valuable as the guarantee of high-speed transmission. At present, wireless communications using static spectrum allocation scheme, most of the frequency bands has been assigned to the primary users, and some of the emerging wireless transmission technology can only use unlicensed bands. On the other hand, there exit plenty of spectrum holes among licensed bands. The cognitive radio technology can detect and utilize the spectrum holes without disturbing the primary users. And it has received extensive attention and research in recent years because of the advantage of enhancing the frequency utilization efficiency. Due to the limitation of technology and policy, cognitive users cannot make full use of spectrum holes. We call the capacity that by using the spectrum holes as effective capacity. It can be a measure of cognitive radio system performance.Firstly we can find that effective capacity subject to the influence of spectrum sensing and spectrum allocation. Due to the errors, there exist situations of missed detections and false alarms in spectrum sensing. Missed diagnosis could cause a conflict with the primary users, and false alarm makes the cognitive user miss the available spectrum opportunities. Different spectrum management and allocation schemes make the spectral efficiency different. This paper studies the impact of effective capacity in the two aspects.Then, we build a model of single channel cognitive radio, which uses the frame structure that consisting of a sensing slot and a transmission slot. By the method of energy detection, we analyze the relationship between sensing time and spectrum sensing accuracy. And the relationship between the effective capacity and the sensing time is discussed. The simulation result shows that the value of the optimal sensing time, which not only can be used as reference for the design of frame structure, but also can obtain the optimal average effective capacity.Next, a multichannel cognitive radio model is built that based on the single channel model. And cognitive frame is improved by increasing the time of channel handoff. The channel is divided into three states, which are idle, occupancy by a primary user or a cognitive user. And the transfer between the various states of the channel is a Markov process. Given primary user’s traffic characteristics, the stationary distribution of system can be obtained. When the primary users that obeying Poisson distribution arrive in system, the cognitive user which not detect the primary users in the current channel, can continue to transmit data. Or it need to switch channel. The probability of successful handoff to idle channel and the outage probability represent the performance of channel switching. Cognitive users can get different effective capacity while meet different situations. We can obtain the average effective capacity combined with the handoff probability. Finally we discuss how to make the system obtain the optimal effective capacity. Through the analysis of the relationship between effective capacity and the sensing time, we get the value of the optimal sensing time in cognitive frame structure. When the total bandwidth of system is fixed, the bandwidth of each channel depends on the number of channel. It is concluded that the best suitable channel number for different licensed network by analyzing the change of effective capacity with the channel number under different primary user traffic. This study provides a reference for the establishment of cognitive radio system.