In this paper, we propose a new discriminatory channel estimation (DCE) scheme to combat active eavesdropping in multiple-input multiple-output (MIMO) decode-and-forward (DF) relay systems. In particular, through cooperative jamming (CJ) and artificial noise (AN) insertion, the discrimination between channel state information (CSI) at the active eavesdropper (Eve) and legitimate users (LUs) can be well ensured. To characterize the performance of the proposed DCE scheme, the closed-form expressions of normalized mean squared error (NMSE) of estimation for both legitimate channel and wiretap channel have been derived. In addition, the power allocation between pilot and AN during channel estimation phase is optimized to further enlarge the CSI discrimination. Simulation results validate the effectiveness of the proposed scheme to achieve a higher secrecy performance, even with an active Eve.
In this paper, we propose a new discriminatory channel estimation (DCE) scheme to combat active eavesdropping in multiple-input multiple-output (MIMO) decode-and-forward (DF) relay systems. In particular, through cooperative jamming (CJ) and artificial noise (AN) insertion, the discrimination between channel state information (CSI) at the active eavesdropper (Eve) and legitimate users (LUs) can be well ensured. To characterize the performance of the proposed DCE scheme, the closed-form expressions of normalized mean squared error (NMSE) of estimation for both legitimate channel and wiretap channel have been derived. In addition, the power allocation between pilot and AN during channel estimation phase is optimized to further enlarge the CSI discrimination. Simulation results validate the effectiveness of the proposed scheme to achieve a higher secrecy performance, even with an active Eve.
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.
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 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 (and, thus, the effective received signal qualities) 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 nonreciprocal 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.
In this paper, we consider decode-and-forward (DF) relay beamforming with imperfect channel state information (CSI), cooperative artificial noise (AN) injection, and finite-alphabet input in the presence of an user and J non-colluding eavesdroppers. The communication between the source and the user is aided by a multiple-input-multiple-output (MIMO) DF relay. We use the fact that a wiretap code consists of two parts: i) common message (non-secret), and ii) secret message. The source transmits two independent messages: i) common message (non-secret), and ii) secret message. The common message is transmitted at a fixed rate R0, and it is intended for the user. The secret message is also intended for the user but it should be kept secret from the J eavesdroppers. The source and the MIMO DF relay operate under individual power constraints. In order to improve the secrecy rate, the MIMO relay also injects artificial noise. The CSI on all the links are assumed to be imperfect and CSI errors are assumed to be norm bounded. In order to maximize the worst case secrecy rate, we maximize the worst case link information rate to the user subject to: i) the individual power constraints on the source and the MIMO relay, and ii) the best case link information rates to J eavesdroppers be less than or equal to R0 in order to support a fixed common message rate R0. Numerical results showing the effect of perfect/imperfect CSI, presence/absence of AN with finite-alphabet input on the secrecy rate are presented.
Achieving secure transmission in wireless network is a significantly important research topic. Classic cryptography encrypts a confidential message to an unreadable cipher message. Thus only the authentic receiver with correct secrecy key is able to decrypt the message. However, with the growing computational capability of adversaries, the requirement of secrecy key generation and distribution is getting stringent. Another method to achieve wireless security, which has attracted much attention in recent years, is through physical layer processing, named physical layer security. In a typical wiretap channel model consisting of a transmitter, a legal receiver and an adversary (illegal receiver), early works have proven that it is possible to achieve positive secrecy rate using multiple antenna techniques, such as beamforming, at the transmitter. In this thesis, we focus on two types of malicious behaviors of the adversary: passive eavesdropping and active attack. For the passive eavesdropping, we first consider the multi-input-single-output (MISO) wiretap channel, in which the legitimate transmitter utilizes the artificial noise aided precoding strategy to maximize the achievable secrecy rate of the channel. Both scenarios of known and unknown channel state information (CSI) for the eavesdropper channels are considered. By deriving the closed-form expression of ergodic secrecy rate, we prove that there exists an optimal power ratio between the information signal and the artificial noise. We then extend our study to a four-node fading channel model in a wireless relay network, which includes two single-antenna users, one single-antenna eavesdropper and one relay node equipped with multiple antennas. By studying the decode-and-forward (DF) scheme, cooperative jamming (CJ) scheme and one proposed hybrid relay scheme, relay-and-jamming (RJ), we aims to find the optimal relay scheme with the condition that the transmitter and the relay node are under individual power constraints and the CSI of the illegitimate channel is only statistically known. Our analysis shows that the hybrid RJ scheme is optimal when the transmitter has a relatively small power budget and/or the transmitter is far away from the intended receiver. Besides the passive eavesdropping, the adversary could also choose the active attack. The pilot spoofing attack is one active attack conducted by the adversary during the channel estimation phase of the legitimate transmission. In practical systems, the pilot signals are usually known \textit{a priori} and repeatedly used information. In this attack, an intelligent adversary spoofs the transmitter on the estimation of CSI by sending the identical pilot signal as the legitimate receiver. By doing so, the adversary could obtain a larger channel rate in the data transmission phase, and also drastically weaken the strength of the received signal at the legitimate receiver if the adversary utilizes large enough power. Due to the serious consequences caused by such an attack, we first propose an energy ratio detector (ERD) that explores the asymmetry of received signal power levels at the transmitter and the legitimate receiver when the attack occurs. Our analysis shows that the ERD could provide very high probability of detection with certain requirement on the false alarming probability. Furthermore, we design a two-way training based scheme to provide not only attack detection but also secure transmission. The two-way training based detector (TWTD) exploits the intrusive component created by the adversary, followed by a secure beamforming-assisted data transmission. In addition to the good detection performance, our scheme could estimate both legitimate and illegitimate channels, with which the beamforming could be designed for data transmission.
Secure communications can be impeded by eavesdroppers in conventional relay systems. This paper proposes cooperative jamming strategies for two-hop relay networks where the eavesdropper can wiretap the relay channels in both hops. In these approaches, the normally inactive nodes in the relay network can be used as cooperative jamming sources to confuse the eavesdropper. Linear precoding schemes are investigated for two scenarios where single or multiple data streams are transmitted via a decode-and-forward (DF) relay, under the assumption that global channel state information (CSI) is available. For the case of single data stream transmission, we derive closed-form jamming beamformers and the corresponding optimal power allocation. Generalized singular value decomposition (GSVD)-based secure relaying schemes are proposed for the transmission of multiple data streams. The optimal power allocation is found for the GSVD relaying scheme via geometric programming. Based on this result, a GSVD-based cooperative jamming scheme is proposed that shows significant improvement in terms of secrecy rate compared to the approach without jamming. Furthermore, the case involving an eavesdropper with unknown CSI is also investigated in this paper. Simulation results show that the secrecy rate is dramatically increased when inactive nodes in the relay network participate in cooperative jamming.
The two-way training discriminatory channel estimation (DCE) for non-reciprocal wireless MIMO channels can efficiently differentiate the performance at the legitimate receiver (Bob) and the eavesdropper (Eve). Eve's channel estimation performance is deliberately degraded by the artificial noise (AN) inserted into the training sequence. However, it ignores the possibility that Eve can derive any useful information which can be used to reduce the AN interference during the round-trip stage. In this paper, we discuss the feasibility that Eve can derive certain helpful information from its observations during the two-way training DCE. Then, we propose a pseudo downlink channel (PDC) scheme for Eve to improve its channel estimation performance, which collapses the security of legitimate receiver.