IRS-Aided Secure NOMA Networks Against Internal and External Eavesdropping

Abstract

Intelligent reflecting surface (IRS) is a promising technology which can be integrated with non-orthogonal multiple access (NOMA) to improve the secrecy performance. In this paper, we propose two IRS-aided schemes to enhance the security of NOMA networks for the internal and external eavesdropping, respectively. First, to deal with an internal untrusted user, the secrecy rate maximization problem is formulated by jointly optimizing the active and passive beamforming, while meeting the quality of service (QoS) demand of the untrusted user, decoding order constraints, and unit modulus constraints of IRS elements. Furthermore, considering a worse scenario with both internal and external eavesdroppers, we minimize the transmit power of legitimate signals with both users’ QoS demands satisfied. In this way, the confidential information leakage will be mitigated and more transmit power can be allocated as artificial jamming to attenuate the eavesdropping. To tackle the non-convex optimization, the original problem in each scheme is first decomposed into two subproblems, which are approximated into convex ones via the semidefinite relaxation (SDR). Then, by means of alternating optimization, the suboptimal solutions to the original problems can be obtained. Simulation results demonstrate the superiority of the proposed schemes against the challenging internal and external eavesdropping by combining IRS and NOMA.