A Semiblind Two-Way Training Method for Discriminatory Channel Estimation in MIMO Systems

TL;DR

This paper introduces a semiblind two-way training method for discriminatory channel estimation in MIMO systems, enhancing security by degrading eavesdropper channel estimation and optimizing power allocation, outperforming existing schemes especially under attack.

Contribution

Proposes a novel whitening-rotation based semiblind two-way training scheme for DCE in MIMO systems with analytical NMSE performance and robust against pilot contamination attacks.

Findings

Achieves better DCE performance than LMMSE-based schemes.

Provides closed-form NMSE expressions for both LR and UR.

Demonstrates robustness against pilot contamination attacks.

Abstract

Discriminatory channel estimation (DCE) is a recently developed strategy to enlarge the performance difference between a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. Specifically, it makes use of properly designed training signals to degrade channel estimation at the UR which in turn limits the UR's eavesdropping capability during data transmission. In this paper, we propose a new two-way training scheme for DCE through exploiting a whitening-rotation (WR) based semiblind method. To characterize the performance of DCE, a closed-form expression of the normalized mean squared error (NMSE) of the channel estimation is derived for both the LR and the UR. Furthermore, the developed analytical results on NMSE are utilized to perform optimal power allocation between the training signal and artificial noise (AN). The advantages of our proposed DCE scheme are two folds: 1) compared to the existing DCE scheme based on the linear minimum mean square error (LMMSE) channel estimator, the proposed scheme adopts a semiblind approach and achieves better DCE performance; 2) the proposed scheme is robust against active eavesdropping with the pilot contamination attack, whereas the existing scheme fails under such an attack.