Wireless Channel Scene Recognition Method Based on an Autocorrelation Function and Deep Learning

Abstract

Wireless channel scene recognition plays a key role in cognitive radio (CR) mobile communication systems. This paper proposes a wireless channel scene identification framework based on the autocorrelation function and deep learning. First, a feature extraction (FE) method is developed to perform a channel scene date analysis based on the autocorrelation function (AF). The AF is used to realize the FE method because it can be combined with Fourier transform (FT) to accurately extract the characteristics accurately from a time-varying channels scene. Second, a deep belief network (DBN) with a robust learning approach is introduced to perform wireless channel scene recognition. A novel learning architecture is employed, which combines the feature parameter and classification techniques to achieve a high classification correct recognition rate. Third, the k-step contrastive divergence (CD-k) algorithm is introduced as the preliminary training method to optimize the traditional DBN network. This method can effectively calculate the logarithmic gradient of the Boltzmann machine. Moreover, the up-down optimization algorithm is applied to optimize the network parameters. Finally, the theoretical implementation is described in detail, and the method is verified by constructing an experiment platform for an engineering application. The experimental results indicate that the proposed classifier is an excellent approach to realize channel scene recognition through advanced methods. The classification accuracy of the proposed approach is higher than that of several existing techniques.