Research On Spectrum Sensing And Power Control Of Cognitive Radio

With the increase in the number of wireless access devices and the rapid development of various mobile services,static spectrum resource control methods can no longer meet the current communication needs.Cognitive radio can intelligently schedule idle spectrum among wireless users,thereby improving its utilization rate.Spectrum sensing and power control technology is the key to the realization of cognitive radio system functions.In the traditional,the spectrum sensing of cognitive radio single-node only occurs in a specific time period,ignoring the support effect of the information received by the secondary users in other time periods on the spectrum sensing.The multi-objective particle swarm optimization algorithm is an efficient multiobjective optimization algorithm,which is very suitable for solving the power control optimization problem of substrate-based cognitive radio,but it is easier to fall into the local optimum.In view of the above problems,this thesis mainly completes the following research work:First,the research and simulation of the energy detection algorithm based on the transmission stage information for spectrum sensing in the opportunistic cognitive radio system,and an optimal detection threshold calculation method.Secondly,Gaussian mutation mechanism is introduced into the particle position update of the traditional multi-objective particle swarm optimization algorithm,and a density-based reference line method is used to maintain the solution set file of the algorithm.The improved algorithm is applied to solve the secondary user power control optimization problem of cognitive radio.Simulation verification shows that compared with the traditional spectrum sensing energy detection algorithm,the algorithm described in this article performs spectrum sensing through the secondary utilization of the information received in the transmission phase,which saves the time spent on spectrum sensing from the system mechanism level and effectively improve the communication efficiency between secondary users.The improved particle swarm optimization algorithm is used to control the transmission power of the secondary user in the substrate-based cognitive radio system.Under the same conditions,the secondary user available transmission power and signal-to-noise ratio are significantly better than the traditional particle swarm algorithm under the same conditions.