Research Of Spectrum Sensing In Cognitive Radio

Over the last decade, wireless technologies have grown rapidly and more and more spectrum resources are needed to support numerous emerging wireless services. Within the current spectrum regulatory framework, however, all of the frequency bands are exclusively allocated to specific services and no violation from unlicensed users is allowed. The issue of spectrum scarcity becomes more obvious and worries the wireless system designers and telecommunications policy makers. Interestingly, a recent survey of the spectrum utilization made by the Federal Communications Commission (FCC) has indicated that the actual licensed spectrum is largely under-utilized in vast temporal and geographic dimensions.In order to solve the conflicts between spectrum scarcity and spectrum under-utilization, cognitive radio technology was recently proposed. It can improve the spectrum utilization by allowing secondary networks (users) to borrow unused radio spectrum from primary licensed networks (users)or to share the spectrum with the primary networks (users). As an intelligent wireless communication system, a cognitive radio is aware of the radio frequency environment. It selects the communication parameters (such as carrier frequency, bandwidth, and transmission power) to optimize the spectrum usage and adapts its transmission and reception accordingly. One of the most critical components of cognitive radio technology is spectrum sensing. By sensing and adapting to the environment, a cognitive radio is able to fill in spectrum holes and serve its users without causing harmful interference to the licensed user. This dissertation deals with the problem of spectrum sensing in cognitive radio. The main contributions are as follows:1.Study the minimum detection of overhead problem of energy-constrained cognitive networks. Taking into account each cognitive radio received the difference of SNR, analyzing the performance of the algorithm counts, prove that when the radio station is infinity, as long as under certain conditions, the detection probability of system Pd= 1, the false alarm probability of system Pf= 0. When the radio station is finite, select all stations involved in perception will not make the system optimal, but should choose the part of the radio which has the higher SNR to percept, and gives the expression for minimum station number which can satisfy the system demand. On this basis, it can deduce the conditions which required for a length of sensing under the conditions of detection of overhead constraint for the minimization system channel. Simulation results show that in the case of cognitive radio received the same of the average perceived SNR, if selecting the better performance of users, it will have the corresponding to the minimum required for the number of sensing stations and the length of sensing to minimize the overhead of the system sense.2.The technology of cooperation spectrum sensing can improve the system performance through the co-operation detection of multiple users. However, it consumes a lot of system resources with an increase in the number of users involved in sensing. In order to balance the sense performance and efficiency of system resource, taking into account the sense overhead and system efficiency, cooperative spectrum sensing optimization algorithm which has based maximum system utility was proposed. Simulation results show that in the context to meet a given system detection probability, there is a corresponding number of sensing radio and sensing length to maximum cognitive system throughput.3.Traditional sensing approaches is based on a passive avoidance mode. Any secondary user communicates via one channel until detecting any primary user. Because of the inherent delay in detection, secondary users could disrupt the operation at nearby primary users. Therefore, an active smart spectrum switching algorithm was proposed. First, it analyzes the selection of the cognitive users'available bandwidth under the conditions of multi-channel. In the premises of parallel sense and sequential sense, taking into account the sense overhead, the numbers of required channels which have the maximum throughput were concluded. Next, in the active spectrum sensing mode, a smart spectrum switching algorithm was proposed through the channel prediction. Simulation shows that the proposed method can reduce the number of channel switching by up to 20%, and minimize disruptions to primary users.