Discriminatory Channel Estimation Research Articles

Low-complexity Discriminatory Channel Estimation Based on Training Sequence Contamination

Low-complexity Discriminatory Channel Estimation Based on Training Sequence Contamination

Security-Oriented Pilot and Data Transmission for URLLC in Mission-Critical IoT Scenarios

In this paper, we focus on the joint design of channel training and data transmission for secure ultra-reliable and low-latency communications (URLLC) in mission-critical Internet-of-Things (IoT) scenarios, e.g., intelligent transportation and remote control. Specifically, we consider a multiple-input multiple-output multi-antenna eavesdropper (MIMOME) system, and the role of artificial noise (AN) for securing URLLC in this system is studied. In the channel training phase, we use the two-way discriminatory channel estimation (DCE) protocol with AN injection to suppress the channel estimation accuracy at eavesdropper. Meanwhile, the AN-assisted secrecy beamforming scheme is adopted to mask the confidential data signals. Following by the security enhancements above, we provide a quantitative definition of achievable effective secrecy rate (AESR) to measure the performance of our URLLC system with imperfect channel state information (CSI) and short-packet feature. Then, a non-asymptotic closed-form lower bound of AESR is provided to make the numerical calculation tractable, and the asymptotic system performance in the high-SNR regime is also studied to gain a comprehensive insight. Based on the cyclic coordinated search method, we propose an iterative resource allo-cation algorithm to maximize the AESR of our system, where the blocklength and transmit power assigned to the reverse/forward pilots, confidential data signals and AN are jointly optimized. In addition, numerical results reveal the AESR performance of our URLLC system for different system parameters, and demonstrate the convergence and superiority of our proposed algorithm.

DCE-Based Secure Transmission for Massive MIMO Relay System Against Active Eavesdropper

Discriminatory channel estimation (DCE) based on two-way training can effectively enhance physical layer security in multiple-input multiple-output (MIMO) systems. In this paper, considering an active eavesdropping scenario where the eavesdropper (Eve) can adaptively switch between passive eavesdropping, and active jamming modes, we propose a new DCE-based secure transmission scheme with cooperative jamming, and artificial noise (AN) insertion in massive MIMO decode-and-forward (DF) relay systems. To characterize the performance of the proposed scheme, firstly, the closed-form expressions for normalized mean squared error (NMSE) of legitimate channels, and wiretap channels are derived. Then, according to the NMSE obtained during channel estimation phase, we design different secure transmission schemes, and derive the approximative expressions of average security rate with respect to the behavior of active Eve, including malicious jamming, and passive eavesdropping, respectively. Finally, the power allocation at source, and relay is jointly optimized to minimize the average signal to interference plus noise ratio (SINR) at the Eve while ensuring that of the legitimate receivers to be large than a threshold for the quality-of-service (QoS) requirement. Numerical results show that the proposed DCE-based secure transmission scheme can effectively discriminate the CSI between legitimate receivers, and Eve even in the presence of an active Eve, and therefore achieve a better security performance than that without DCE.

Link-Signature-Based Discriminatory Channel Estimation (LS-DCE) for Physical Layer Security in Stationary and Mobile OFDM Transceivers

In this work, we propose a physical layer security (PLS) scheme for orthogonal frequency-division multiplexing (OFDM) based transceivers, termed link-signature-based discriminatory channel estimation (LS-DCE). The proposed scheme exploits the space-time uniqueness of the channel responses (i.e. link-signatures) at different transceiver pairs to facilitate a reliable signal reception at legitimate receivers, while equivocating reception at non-legitimate adversaries. While most PLS studies focus on transmitted data to provide such an equivocation, LS-DCE focuses on channel estimations. Since the quality of estimates impacts the overall performance, LS-DCE facilitates distinctive detection error probabilities by enforcing a poor channel estimation at non-legitimate receivers as compared to legitimate ones. To validate the effectiveness of the proposed scheme, analytical, simulated, and experimental evaluations are performed. First, a theoretical analysis is established in terms of the probability of bit error (PBE) at the legitimate and non-legitimate receivers. Then, indoor/outdoor stochastic and deterministic channel models are adopted using correlated Nakagami-m simulation model and software-defined radio (SDR) testbed, respectively. With $-10\,{\rm dB}$ signal-to-noise ratio (SNR), it is found that a spatial correlation $\leq 0.1$ between the legitimate and non-legitimate channels results in an average PBE of 0.48, 0.48, and 0.49 for BPSK, QPSK, and 16-QAM, respectively, at the non-legitimate receiver; and 0.45, 0.46, and 0.48 with $\text{50}\,{\rm dB}$ SNR.

In-Band Full-Duplex Discriminatory Channel Estimation Using MMSE

This paper proposes full-duplex transmissions from legitimate nodes to achieve channel estimation performance deterioration at an eavesdropper as compared to the legitimate receiver. The proposed discriminatory channel estimation (DCE) technique comprises of two stages where, in the first stage, the self-interference channel is estimated by the respective legitimate nodes. Followed by in-band full-duplex transmission from both legitimate nodes for channel estimation at legitimate nodes, while providing equivocation at the eavesdropper due to the superposition of two signals. The discrimination of channel estimation performance provides secrecy against the passive eavesdropper while delivering information to the legitimate receiver. We provide the mean square error (MSE) to indicate the performance achieved by linear minimum mean square error (LMMSE) estimators. We have also provided bit error rate (BER), and secrecy capacity analysis to indicate the performance of secure communication achieved by securing the channel estimates from the eavesdropper. The BER analysis shows that for proposed DCE, BER at the eavesdropper is close to 0.1 while the legitimate node is able to robustly decode the information. Finally, simulation results show that the proposed DCE outperforms existing DCE techniques for the considered scenario.

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.

Two-Way Training for Discriminatory Channel Estimation in Wireless MIMO Systems

This work examines the use of two-way training to efficiently discriminate the channel estimation performances at a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. This work improves upon the original discriminatory channel estimation (DCE) scheme proposed by Chang et al where multiple stages of feedback and retraining were used. While most studies on physical layer secrecy are under the information-theoretic framework and focus directly on the data transmission phase, studies on DCE focus on the training phase and aim to provide a practical signal processing technique to discriminate between the channel estimation performances at LR and UR. A key feature of DCE designs is the insertion of artificial noise (AN) in the training signal to degrade the channel estimation performance at UR. To do so, AN must be placed in a carefully chosen subspace based on the transmitter's knowledge of LR's channel in order to minimize its effect on LR. In this paper, we adopt the idea of two-way training that allows both the transmitter and LR to send training signals to facilitate channel estimation at both ends. Both reciprocal and non-reciprocal channels are considered and a two-way DCE scheme is proposed for each scenario. {For mathematical tractability, we assume that all terminals employ the linear minimum mean square error criterion for channel estimation. Based on the mean square error (MSE) of the channel estimates at all terminals,} we formulate and solve an optimization problem where the optimal power allocation between the training signal and AN is found by minimizing the MSE of LR's channel estimate subject to a constraint on the MSE achievable at UR. Numerical results show that the proposed DCE schemes can effectively discriminate between the channel estimation and hence the data detection performances at LR and UR.

Training Sequence Design for Discriminatory Channel Estimation in Wireless MIMO Systems

This paper proposes a training-based channel estimation scheme for achieving quality-of-service discrimination between legitimate and unauthorized receivers in wireless multiple-input multiple-output (MIMO) channels. The proposed method has applications ranging from user discrimination in wireless TV broadcast systems to the prevention of eavesdropping in secret communications. By considering a wireless MIMO system that consists of a multiple-antenna transmitter, a legitimate receiver (LR) and an unauthorized receiver (UR), we propose a multi-stage training-based discriminatory channel estimation (DCE) scheme that aims to optimize the channel estimation performance of the LR while limiting the channel estimation performance of the UR. The key idea is to exploit the channel estimate fed back from the LR at the beginning of each stage to enable the judicious use of artificial noise (AN) in the training signal. Specifically, with knowledge of the LR's channel, AN can be properly superimposed with the training data to degrade the UR's channel without causing strong interference on the LR. The channel estimation performance of the LR in earlier stages may not be satisfactory due to the inaccuracy of the channel estimate and constraints on the UR's estimation performance, but can improve rapidly in later stages as the quality of channel estimate improves. The training data power and AN power are optimally allocated by minimizing the normalized mean-square error (NMSE) of the LR subject to a lower limit constraint on the NMSE of the UR. The proposed DCE scheme is then extended to the case with multiple LRs and multiple URs. Simulation results are presented to demonstrate the effectiveness of the proposed DCE scheme.