Securing Downlink Massive MIMO-NOMA Networks With Artificial Noise

Abstract

In this paper, we focus on securing the confidential information of massive\nmultiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA)\nnetworks by exploiting artificial noise (AN). An uplink training scheme is\nfirst proposed with minimum mean squared error estimation at the base station.\nBased on the estimated channel state information, the base station precodes the\nconfidential information and injects the AN. Following this, the ergodic\nsecrecy rate is derived for downlink transmission. An asymptotic secrecy\nperformance analysis is also carried out for a large number of transmit\nantennas and high transmit power at the base station, respectively, to\nhighlight the effects of key parameters on the secrecy performance of the\nconsidered system. Based on the derived ergodic secrecy rate, we propose the\njoint power allocation of the uplink training phase and downlink transmission\nphase to maximize the sum secrecy rates of the system. Besides, from the\nperspective of security, another optimization algorithm is proposed to maximize\nthe energy efficiency. The results show that the combination of massive MIMO\ntechnique and AN greatly benefits NOMA networks in term of the secrecy\nperformance. In addition, the effects of the uplink training phase and\nclustering process on the secrecy performance are revealed. Besides, the\nproposed optimization algorithms are compared with other baseline algorithms\nthrough simulations, and their superiority is validated. Finally, it is shown\nthat the proposed system outperforms the conventional massive MIMO orthogonal\nmultiple access in terms of the secrecy performance.\n