Identification method of wireless communication suppression interference signal

A interference recognition algorithm based on Grey Wolf Optimization Correlation Vector Machine (GWO-RVM) is proposed for suppressing interference signals in wireless communication. Firstly, singular value decomposition is applied to denoise the interference signal that cannot be suppressed by communication. To avoid the problem of low reliability of interference pattern recognition caused by a single interference feature, this paper normalizes the denoised multi pattern interference signals from the perspective of data redundancy, and synthesizes multidimensional feature vectors to train the RVM recognition model. To improve the classification accuracy of wireless interference signals and enhance the global optimization performance of the model, the Grey Wolf Optimization (GWO) algorithm is adopted to optimize the parameters of the RVM recognition model. And compared with GA-RVM model and PSO-RVM model, the experimental verification showed that the recognition accuracy based on GWO-RVM model was the best, reaching 92.8%.