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In this work, interference alignment is designed for peer-to-peer underlay MIMO cognitive radio network in which single primary user (PU) coexists with multiple secondary users (SUs). The proposed scheme chooses the transmit precoding and receiver interference subspace to minimize the total interference leakage while limiting the interference level to the PU. With perfect knowledge of local channel information, the optimization problem is solved iteratively, in which the transmitter and receivers take turns to update the precoding and interference receiving matrices, respectively. It is proven that the algorithm converges monotonically, and simulation results reveal the effectiveness of the proposed interference alignment scheme for cognitive radio network.

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Limited-angle computed tomography (CT) has arisen in some medical and industrial applications. It is also a challenging problem since some scan views are missing and the directly reconstructed images often suffer from severe distortions. For such kind of problems, we analyze the features of limitedangle CT images and propose a multi-scale dilated convolution neural network (MSD-CNN) to correct the artifacts and to restore the image. In this network, the dilated convolution layer and multi-scale pooling layer are combined to form a group and exited in the whole encoder-decoder process. Since the dilated convolutions support an exponential expansion of the receptive field without losing resolution and coverage, the obtained artifact features possess the multi-scale characteristic. Furthermore, to improve the effectiveness and accuracy of the training step, we employ a preprocessing method, which extracts image patches. Numerical experiments verify the out-performance of the proposed method compared with some conventional methods, such as Unet based deep learning,TV-and L 0-based optimization methods. INDEX TERMS Limited-angle tomography, artifact correction, multi-scale, dilated convolution.

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