Learning the ECSI from binary signals for cognitive multicast secure beamforming

Abstract

This paper investigates cognitive single-group multicast secure beamforming (SGMC-SBF) in multicast scenario where an eavesdropper who acts as a regular user seeks to intercept the multicast service without authorization. This study emphasizes that the transmitter iteratively learns the eavesdropper’s spatial correlation matrix from the accumulated binary feedback on received signal-to-noise-ratio. In each iteration, the transmitter learns the eavesdropper’s spatial correlation matrix based on the historical beamformings and the historical binary feedback information, which is then used to design the optimal beamforming that will be used to learn eavesdropper’s spatial correlation matrix in the next iteration. Without loss of generality, it is assumed that the transmitter knows instantaneous channel state information (CSI) of the legitimate users (LCSI), but not the instantaneous or statistical CSI of the eavesdropper (ECSI). For comparison, we also established the corresponding genie-aided SGMC-SBF with perfect ECSI and two traditional robust schemes with erroneous and statistical ECSI, respectively. The numerical results verify that the proposed cognitive SGMC-SBF are feasible solutions that provide excellent performance.