Visible to the public Artificial-Noise-Aided Robust Beamforming for MISOME Wiretap Channels with Security QoS

TitleArtificial-Noise-Aided Robust Beamforming for MISOME Wiretap Channels with Security QoS
Publication TypeConference Paper
Year of Publication2019
AuthorsLiu, Xiaochen, Gao, Yuanyuan, Zang, Guozhen, Sha, Nan
Conference Name2019 IEEE 19th International Conference on Communication Technology (ICCT)
Keywordsantenna arrays, array signal processing, artificial noise, artificial-noise-aided robust beamforming, channel state information, composability, concave programming, convex programming, estimation errors, formulated design problem, linear matrix inequalities, Metrics, minimum allowable signal-to-interference, MISOME wiretap channels, multiantenna transmitter, multiple multiantenna eavesdroppers, physical-layer security, power saving, privacy, pubcrawl, quality of service, radio receivers, radio transmitters, Resiliency, RJOBF, robust beamforming, robust joint optimal, secret signal, security communication, security QoS, security quality, signal processing security, single-antenna receiver, SINR, telecommunication security, transmit power, wireless channels
AbstractThis paper studies secure communication from a multi-antenna transmitter to a single-antenna receiver in the presence of multiple multi-antenna eavesdroppers, considering constraints of security quality of service (QoS), i.e., minimum allowable signal-to-interference-and-noise ratio (SINR) at receiver and maximum tolerable SINR at eavesdroppers. The robust joint optimal beamforming (RJOBF) of secret signal and artificial noise (AN) is designed to minimize transmit power while estimation errors of channel state information (CSI) for wiretap channels are taken into consideration. The formulated design problem is shown to be nonconvex and we transfer it into linear matrix inequalities (LMIs) along with semidefinite relaxation (SDR) technique. The simulation results illustrate that our proposed RJOBF is efficient for power saving in security communication.
Citation Keyliu_artificial-noise-aided_2019