Abstract
This letter investigates a binary learning mechanism for statistical eavesdroppers’ channel state information (SECSI), in which the transmitter utilizes one-bit signal-to-noise-ratio feedback to constantly learn the channel correlation matrices of the eavesdropping links without any prior SECSI. Correspondingly, with the updated SECSI estimate, the optimal single-group multicast secure beamforming (SGMC-SBF) is determined and probed continually for multicast secrecy rate maximization. Simulation results show that this convergent cognitive strategy could not only achieve higher ergodic multicast secrecy rate than the worst-case robust SGMC-SBF with imperfect SECSI, but also approach the ideal performance achieved by perfect SECSI.
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