Adaptive BAA Switching by CNN-Based Spectrogram Learning in LTE-WLAN Coexistence

This paper validates the concept of spectrum superposing through blind adaptive array (BAA) inter-system interference suppression aided by blind interference estimation. Our proposed framework adaptively switches the BAA algorithm according to the output of the estimator, which classifies the polarity of signal-to-interference power ratio (SIR) trained by convolutional neural network using 2-dimensional time-frequency representation, i.e., spectrogram, of the received signal. Suppose LTE and wireless LAN coexistence scenario; simulation results show that the proposed scheme successfully selects the appropriate BAA algorithm to effectively suppress inter-system interference.