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

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.Comment: accepted for publication in IEEE Transactions on Communication

Blind extraction using fractional lower-order statistics

In traditional method to blindly extract interesting source signals sequentially, the second-order or higher-order statistics of signals are often utilized. However, for impulsive sources, both of the second-order and higher-order statistics may degenerate. Therefore, it is necessary to exploit new method for the blind extraction of impulsive sources. Based on the best compression-reconstruction principle, a novel model is proposed in this work, together with the corresponding algorithm. The proposed method can be used for blind extraction of sources which are distributed from alpha stable process. Simulations are given to illustrate availability and robustness of our algorithm

Dispersed operating time control of a mechanical switch actuated by an ultrasonic motor

The ultrasonic motor is an uncertain time-varying nonlinear system because of the nonlinearity of the piezoelectric material, the friction and the temperature. For example, the operating time of the mechanical switch actuated by the ultrasonic motor in regular stroke is highly dispersed. Unfortunately, it is difficult to establish accurate mathematical model. In this paper, an analytical autoregressive process model (AR) is employed to identify and control the ultrasonic motor. First of all, dispersed operating time of the mechanical switch actuated by the ultrasonic motor is investigated. Then, the AR model is established to predict the operating time of the ultrasonic motor on the basis of the statistical data to reduce the nonlinear behavior of the ultrasonic motor, and to improve the accuracy and obtain a good time response of the switch. The simulation results are agreed with experimental results, confirming the effectiveness of proposed model. Furthermore, we adopt the predicted result of the AR model to control the mechanical switch actuated by the ultrasonic motor. The analytical investigation is fulfilled with two target operating time ranges, namely 12 ms and 24 ms. Comparison of the results obtained from the AR model and the experimentation reveal that the standard deviations are less than 95.3 μs and 102.7 μs with maximum errors equal to 0.41 % and 0.44 % respectively. Thereby, the proposed dispersed operating time control is performed. Findings indicate that the maximum errors for the operating time of the mechanical switch are less than 140 μs and 110 μs with ±0.85 % and ±0.42 % respectively