Eavesdropping against artificial noise: Hyperplane clustering

Wireless secure communication using artificial noise can guarantee security when the transmitter has more antennas than the eavesdropper. Contrarily, this paper considers the problem of eavesdropping when sufficient antennas were equipped. First, we prove that the receiving scrambling signals are distributed within parallel hyperplanes. Second, based on this signal signature, we propose Hyperplane Clustering algorithm (HC) for real-time eavesdropping. The HC algorithm uses parallel hyperplanes in signal space to approximate receiving signal points, extract signal features and thus intercept messages. Simulations show that the HC algorithm has the benefits of lower complexity, and better anti-noise performance, compared with the existing MUSIC-like algorithms.