Secrecy Sum-Rates with Regularized Channel Inversion Precoding under Imperfect CSI at the Transmitter

TL;DR

This paper analyzes the secrecy sum-rate performance of regularized channel inversion precoding in MISO broadcast channels with imperfect CSIT, providing asymptotic approximations and practical guidelines for FDD and TDD systems.

Contribution

It introduces an asymptotic approximation for secrecy sum-rate under imperfect CSIT and derives feedback and training strategies to optimize high-SNR secrecy performance.

Findings

Asymptotic secrecy sum-rate approximation is accurate for finite systems.

Feedback bits scale with SNR to maintain high-SNR rate gap.

Optimal training duration maximizes high-SNR secrecy sum-rate.

Abstract

In this paper, we study the performance of regularized channel inversion precoding in MISO broadcast channels with confidential messages under imperfect channel state information at the transmitter (CSIT). We obtain an approximation for the achievable secrecy sum-rate which is almost surely exact as the number of transmit antennas and the number of users grow to infinity in a fixed ratio. Simulations prove this anaylsis accurate even for finite-size systems. For FDD systems, we determine how the CSIT error must scale with the SNR, and we derive the number of feedback bits required to ensure a constant high-SNR rate gap to the case with perfect CSIT. For TDD systems, we study the optimum amount of channel training that maximizes the high-SNR secrecy sum-rate.