Large System Analysis of Linear Precoding in MISO Broadcast Channels with Confidential Messages

Abstract

In this paper, we study the performance of regularized channel inversion (RCI) precoding in large MISO broadcast channels with confidential messages (BCC). We obtain a deterministic approximation for the achievable secrecy sum-rate which is almost surely exact as the number of transmit antennas $M$ and the number of users $K$ grow to infinity in a fixed ratio $\beta =K/M$. We derive the optimal regularization parameter $\xi$ and the optimal network load $\beta$ that maximize the per-antenna secrecy sum-rate. We then propose a linear precoder based on RCI and power reduction (RCI-PR) that significantly increases the high-SNR secrecy sum-rate for $1<\beta <2$. Our proposed precoder achieves a per-user secrecy rate which has the same high-SNR scaling factor as both the following upper bounds: (i) the rate of the optimum RCI precoder without secrecy requirements, and (ii) the secrecy capacity of a single-user system without interference. Furthermore, we obtain a deterministic approximation for the secrecy sum-rate achievable by RCI precoding in the presence of channel state information (CSI) error. We also analyze the performance of our proposed RCI-PR precoder with CSI error, and we determine how the error must scale with the SNR in order to maintain a given rate gap to the case with perfect CSI.

to appear IEEE JSAC 2013

References in corpus (4)

• Rethinking the Secrecy Outage Formulation: A Secure Transmission Design Perspective
• Secrecy Sum-Rates for Multi-User MIMO Regularized Channel Inversion Precoding
• Linear Precoding for Broadcast Channels with Confidential Messages under Transmit-Side Channel Correlation
• Secrecy Sum-Rates with Regularized Channel Inversion Precoding under Imperfect CSI at the Transmitter

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• Linear Precoding for Broadcast Channels with Confidential Messages under Transmit-Side Channel Correlation
• Large System Analysis of Cognitive Radio Network via Partially-Projected Regularized Zero-Forcing Precoding