Channel Correlation Map based Indoor-to-outdoor Artificial Noise Design and Secrecy Analysis

Generating random artificial noise (AN) in the null space of the legitimate channel is an effective way to achieve secrecy in wireless communication when the locations and channels of eavesdroppers are not known. In this paper, we propose an AN design based on the channel correlation map assuming passive eavesdroppers (Eves) for indoor-to-outdoor scenario. Channel correlation map is a geographical map with overlay knowledge of long-term channel correlation matrices for possible Eves' locations. This knowledge is learned by legitimate users passing through these areas in the past. Different from the existing secrecy analyses with passive Eves, correlated transmit antenna arrays are assumed. Our AN vector is generated by maximizing the secure probability of attaining a pre-determined secrecy level for a certain number of the most dangerous potential Eves, which is found by the correlation map. We give an intuitive view of the secrecy performance for the whole area and show that our strategy enhances the secrecy level of the system when compared to the existing random null space AN method.