Large-System Analysis of Artificial-Noise-Assisted Communication in the Multiuser Downlink: Ergodic Secrecy Sum Rate and Optimal Power Allocation

Security and privacy have become increasingly critical demands in wireless networks, which, however, are particularly susceptible to eavesdropping attacks due to the broadcast nature of radio signals. This paper considers the problem of secure communication in the multiuser downlink with a passive eavesdropper (Eve), whose channel state information (CSI) is unavailable. The transmitter simultaneously transmits concurrent information signals to the users and artificial noise (AN) to Eve. We first assume that all users' CSI is perfectly known by the transmitter and derive a closed-form expression for the ergodic secrecy sum rate (SSR) in the large-system limit. We then use it as an objective function to optimize the power allocation between information signals and the AN. It shows that more power needs to be used for AN when Eve has more antennas and when the system serves fewer users. We also extend the analysis to the imperfect-CSI scenario, where the SSR saturates at high transmit power, and it is better to create more AN than to increase the signal strength when the channel estimation error is large. We derive a scale law of feedback bits (for frequency-division duplexing (FDD) systems) to maintain a constant rate offset compared with the perfect-CSI case and the optimal length of training sequence (for time-division duplexing (TDD) systems) to maximize the effective SSR. It shows that more feedback bits and longer training sequences are required to deal with the eavesdropping problem. Closed-form expressions derived in this paper can reduce the complexity of system analysis and design.