Real interference alignment for the K-user Gaussian interference compound wiretap channel
We study the K-user Gaussian interference wiretap channel with N external eavesdroppers. All the transmitters, receivers and eavesdroppers have a single antenna each. We propose an achievable scheme to lower bound the secure degrees of freedom (d.o.f.) for each transmitter-receiver pair. Our approach is based on the (real) interference alignment technique. Our achievable scheme not only aligns the interference at each receiver to prevent the d.o.f. from vanishing, but also aligns the signals observed by the eavesdroppers to reduce the secrecy penalty. The achievable secure d.o.f. of each transmitter-receiver pair is shown to be equations for almost all channel gains.
In order to improve the correctness of locally linear embedding caused by sparse data,a novel dimensionality reduction algorithm based on neighborhood rival linear embedding was proposed in this paper.According to the statistical information,it determines local linear dynamic range,adopts the cam distribution to find neighbors of data points,and avoids the lack of the direction of neighbor selection.In the case of sparse data sets,the algorithm can effectively obtain local and global information of data.The experiment to test the improved algorithm obtains a good effort of reducing dimension.The experimental results on the image retrieval using the Corel database show the efficiency of the algorithm.