We consider a multi-link interference network that is tapped by an external eavesdropper. To conceal information from the eavesdropper, legitimate links are equipped with transmitter-based friendly jamming (TxFJ) and receiver-based friendly jamming (RxFJ). Each link seeks to maximize its secrecy rate by determining the best power assignment (PA) for the information, TxFJ, and RxFJ signals. Joint optimization of these parameters is a non-convex problem. Hence, we seek sub-optimal solutions. Specifically, we find a lower bound on the allocated power to TxFJ above which positive secrecy is achievable for a given link. Once positive secrecy is achieved, the secrecy rate becomes monotonically increasing in the power at the transmitter (Alice). Therefore, the rest of Alice's power is allocated to the information signal. Despite its sub-optimality, such an approach precludes the possibility of employing successive interference cancellation by the eavesdropper. The RxFJ PA of a link is adjusted using an on-off PA that depends only on the link's local channel state information (CSI). With every link following such a strategy, we model this interaction as a non-cooperative game. We derive sufficient conditions for the uniqueness of the resulting Nash equilibrium. We then propose an algorithm to implement the PA game. Lastly, we relax knowledge of eavesdropper's CSI (E-CSI) and propose a framework that is robust to unknown E-CSI. Our results indicate that this robust framework performs close to when E-CSI is fully known to legitimate links. Moreover, empirically it is shown that the secrecy sum-rate scales with the power budget of transmitters.
We consider a multi-link interference network that is tapped by an external eavesdropper. To conceal information from the eavesdropper, legitimate links are equipped with transmitter-based friendly jamming (TxFJ) and receiver-based friendly jamming (RxFJ). Each link seeks to maximize its secrecy rate by determining the best power assignment (PA) for the information, TxFJ, and RxFJ signals. Joint optimization of these parameters is a non-convex problem. Hence, we seek sub-optimal solutions. Specifically, we find a lower bound on the allocated power to TxFJ above which positive secrecy is achievable for a given link. Once positive secrecy is achieved, the secrecy rate becomes monotonically increasing in the power at the transmitter (Alice). Therefore, the rest of Alice's power is allocated to the information signal. Despite its sub-optimality, such an approach precludes the possibility of employing successive interference cancellation by the eavesdropper. The RxFJ PA of a link is adjusted using an on-off PA that depends only on the link's local channel state information (CSI). With every link following such a strategy, we model this interaction as a non-cooperative game. We derive sufficient conditions for the uniqueness of the resulting Nash equilibrium. We then propose an algorithm to implement the PA game. Lastly, we relax knowledge of eavesdropper's CSI (E-CSI) and propose a framework that is robust to unknown E-CSI. Our results indicate that this robust framework performs close to when E-CSI is fully known to legitimate links. Moreover, empirically it is shown that the secrecy sum-rate scales with the power budget of transmitters.
We propose a distributed interference management method for a single-stream MIMO interference network that is tapped by an external eavesdropper. Along with its information signal, each legitimate transmitter creates a bogus signal, known as transmit-based friendly jamming (TxFJ), to confuse the eavesdropper. Although generating TxFJ protects the link from eavesdropping, it creates interference at other unintended but legitimate links. Using non-cooperative game theory, we design a distributed method for maximizing the sum of secrecy rates. Each link is a player in the game. It seeks to maximize its secrecy rate subject to a given information-rate constraint and power budget. The strategy profile of each player is to control the amount of TxFJ it generates. Because a pure non-cooperative game may not have Nash equilibria that result in (Pareto-)optimal secrecy sum-rate, we propose a modified price-based game, in which each link is penalized for generating interference on other legitimate links. Under the exact knowledge of eavesdropping channels, we show that the price-based game has a comparable secrecy sum-rate to a centralized approach. We then relax the assumption of knowledge of eavesdropping channels and leverage mixed-strategy games to provide robust solutions to the distributed secrecy sum-rate maximization problem.
We consider an interference network tapped by external eavesdropper(s) in which each legitimate transmit-receive pair conceals its communications by using joint transmit-based friendly jamming (TxFJ) and receiver-based friendly jamming (RxFJ). Specifically, TxFJ is realized at the transmit side using MIMO precoding while RxFJ is achieved at the receiver side of each link by leveraging the state-of-the-art self-interference-suppression techniques (allowing a radio to cancel the self-interference effect of its transmit signal). We show that with a careful power allocation between the information signal and TxFJ at the transmit side of each link, the corresponding receiver is able decide on using RxFJ independent of any multi-user interference factor. This ability sets the receivers free from having to measure multi-user interference at eavesdropper(s). With every link following such strategy, we model this interaction as a non-cooperative game. We derive sufficient conditions under which the game admits a unique Nash equilibrium. We then propose a robust version of the game that requires only statistical knowledge of eavesdropping channel.
In this paper, we expand the scope of PHY-layer security by investigating TX-based friendly jamming (FJ) for the wiretap channel in multi-link settings. For the single-link scenario, creating a TX-based FJ is an effective and practical method in improving the secrecy rate. In a multi-link setting, several information signals must be transmitted simultaneously. Thus, the design must guarantee that the FJ signal of a given transmitter does not interfere with unintended but legitimate receivers. Under the assumption of exact knowledge of the eavesdropping channel, we first propose a distributed price-based approach to improve the secrecy sum-rate of a two-link network with one eavesdropper while satisfying an information-rate constraint for both link. Simulations show that price-based FJ control outperforms greedy FJ, and is close to the performance of a centralized approach. Next, we propose a method based on mixed strategic games that can offer robust solutions to the distributed secrecy sum-rate maximization problem under the assumption of an unknown eavesdropping channel. Lastly, we use simulations to show that in addition to outperforming the greedy approach, our robust optimization also satisfies practical network considerations. In particular, the transmission time for the robust optimization can be determined flexibly to match the channel's coherence time.
In this paper, we consider a two-way wiretap channel in the presence of Multi-Input Multi-Output Multi-antenna Eve (MIMOME), where both nodes (Alice and Bob) transmit and receive in an in-band full-duplex manner and Eve tries to decode the information exchanged between Alice and Bob. For this system with keyless security, we provide artificial noise (AN) based signal design while assuming AN to be known/unknown at the receivers (Alice and Bob). Power allocation problems are formulated with the objective of maximization of sum secrecy rates subject to the transmit power constraint. Accounting for non-ideal self-interference cancellation, a successive convex approximation-based algorithm for the solution for the cases of full CSI, and limited CSI knowledge, including partial information at Eve. For the limited CSI case, numerical ergodic rates are optimized. Simulation results show that with power constraints satisfied, secrecy rates are maximized, and with only partial information at Eve, secrecy rates are improved accordingly.
We consider joint optimization of artificial noise (AN) and information signals in a MIMO wiretap interference network, wherein the transmission of each link may be overheard by several MIMO-capable eavesdroppers. Each information signal is accompanied with AN, generated by the same user to confuse nearby eavesdroppers. Using a noncooperative game, a distributed optimization mechanism is proposed to maximize the secrecy rate of each link. The decision variables here are the covariance matrices for the information signals and ANs. However, the nonconvexity of each link's optimization problem (i.e., best response) makes conventional convex games inapplicable, even to find whether a Nash equilibrium (NE) exists. To tackle this issue, we analyze the proposed game using a relaxed equilibrium concept, called quasi-NE (QNE). Under a constraint qualification condition for each player's problem, the set of QNEs includes the NE of the proposed game. We also derive the conditions for the existence and uniqueness of the resulting QNE. It turns out that the uniqueness conditions are too restrictive, and do not always hold in typical network scenarios. Thus, the proposed game often has multiple QNEs, and convergence to a QNE is not always guaranteed. To overcome these issues, we modify the utility functions of the players by adding several specific terms to each utility function. The modified game converges to a QNE even when multiple QNEs exist. Furthermore, players have the ability to select a desired QNE that optimizes a given social objective (e.g., sum rate or secrecy sum rate). Depending on the chosen objective, the amount of signaling overhead as well as the performance of resulting QNE can be controlled. Simulations show that not only can we guarantee the convergence to a QNE, but also due to the QNE selection mechanism, we can achieve a significant improvement in terms of secrecy sum rate and power efficiency, especially in dense networks.
This paper studies robust transmission schemes for multiple-input single-output (MISO) wiretap channels. Both the cases of direct transmission and cooperative jamming with a helper are investigated with imperfect channel state information (CSI) for the eavesdropper links. Robust transmit covariance matrices are obtained based on worst-case secrecy rate maximization, under both individual and global power constraints. For the case of an individual power constraint, we show that the nonconvex maximin optimization problem can be transformed into a quasi-convex problem that can be efficiently solved with existing methods. For a global power constraint, the joint optimization of the transmit covariance matrices and power allocation between the source and the helper is studied. We also investigate the robust wiretap transmission problem for the case with a quality-of-service constraint at the legitimate receiver. Numerical results show the advantage of the proposed robust design. In particular, for the global power constraint scenario, although cooperative jamming is not necessary for optimal transmission with perfect eavesdropper's CSI, we show that robust jamming support can increase the worst-case secrecy rate and lower the signal to interference-plus-noise ratio at the eavesdropper in the presence of channel mismatches between the transmitters and the eavesdropper.