12 research outputs found
Beamforming and jamming optimization for IRS-aided secure NOMA networks
The integration of intelligent reflecting surface (IRS) and multiple access provides a promising solution to improved coverage and massive connections at low cost. However, securing IRS-aided networks remains a challenge since the potential eavesdropper also has access to an additional IRS reflection link, especially when the eavesdropping channel state information is unknown. In this paper, we propose an IRS-assisted non-orthogonal multiple access (NOMA) scheme to achieve secure communication via artificial jamming, where the multi-antenna base station sends the NOMA and jamming signals together to the legitimate users with the assistance of IRS, in the presence of a passive eavesdropper. The sum rate of legitimate users is maximized by optimizing the transmit beamforming, the jamming vector and the IRS reflecting vector, satisfying the quality of service requirement, the IRS reflecting constraint and the successive interference cancellation (SIC) decoding condition. In addition, the received jamming power is adapted at the highest level at all legitimate users for successful cancellation via SIC. To tackle this non-convex optimization problem, we first decompose it into two subproblems, and then each subproblem is converted into a convex one using successive convex approximation. An alternate optimization algorithm is proposed to solve them iteratively. Numerical results show that the secure transmission in the proposed IRS-NOMA scheme can be effectively guaranteed with the assistance of artificial jamming
Artificial-Noise-Aided Secure MIMO Wireless Communications via Intelligent Reflecting Surface
This paper considers a MIMO secure wireless communication system aided by the
physical layer security technique of sending artificial noise (AN). To further
enhance the system security performance, the advanced intelligent reflecting
surface (IRS) is invoked in the AN-aided communication system, where the base
station (BS), legitimate information receiver (IR) and eavesdropper (Eve) are
equipped with multiple antennas. With the aim for maximizing the secrecy rate
(SR), the transmit precoding (TPC) matrix at the BS, covariance matrix of AN
and phase shifts at the IRS are jointly optimized subject to constrains of
transmit power limit and unit modulus of IRS phase shifts. Then, the secrecy
rate maximization (SRM) problem is formulated, which is a non-convex problem
with multiple coupled variables. To tackle it, we propose to utilize the block
coordinate descent (BCD) algorithm to alternately update the TPC matrix, AN
covariance matrix, and phase shifts while keeping SR non-decreasing.
Specifically, the optimal TPC matrix and AN covariance matrix are derived by
Lagrangian multiplier method, and the optimal phase shifts are obtained by
Majorization-Minimization (MM) algorithm. Since all variables can be calculated
in closed form, the proposed algorithm is very efficient. We also extend the
SRM problem to the more general multiple-IRs scenario and propose a BCD
algorithm to solve it. Finally, simulation results validate the effectiveness
of system security enhancement via an IRS.Comment: To appear in IEEE Transactions on Communication