Dynamic Channel Estimation In Rocognition Radio

In order to meet the needs of modern communication and improve the utilization of existing spectrum resources, cognitive radio technology has entered the stage of history. Cognitive radio technology can make use of a series of dynamic mechanisms to improve the current status of spectrum resource depletion, and the dynamic channel problem of cognitive radio is paid more and more attention.Channel parameter estimation is an important part of spectrum analysis. Its main purpose is to obtain channel state characteristics, which is an important prerequisite for efficient spectrum management. In this paper, a new algorithm for estimating dynamic channel parameters of cognitive radio is proposed which is based on the analysis of the present situation and the theoretical knowledge of the system. This algorithm is mainly aimed at the fast time-varying characteristic of dynamic radio channel, using a new dynamic state space method for dynamic channel estimation.Particle filtering has a unique advantage in dealing with non-linear problems using sequential Monono-Carlo technique. In this paper, the theory of particle filter is used. At the same time, in order to improve the performance of non-Gaussian channel estimation, this paper uses the Alpha stable distribution as the background noise of the second-order autoregressive (AR-2) channel model, and proposes a new channel estimation algorithm which is based on the improved particle swarm optimization. Particle swarm optimization (PSO) algorithm is introduced in the re-sampling stage of Particle Filter. And it is optimized by neighborhood heuristic mechanism and cloud atomization model. The channel estimation algorithm can solve the problem of particle depletion in particle filter, increase particle convergence speed, improve particle sampling precision, and track the original fast time-varying dynamic channel parameters efficiently. The simulation results show that the particle swarm optimization algorithm based on improved PSO can improve the robustness and accuracy of fast time-varying channel estimation in non-Gaussian time-varying channel estimation, which provides a new method for estimating the characteristics of fast time-varying dynamic channel.