This paper investigates secrecy rate optimization for a multicasting network, in which a transmitter broadcasts the same information to multiple legitimate users in the presence of multiple eavesdroppers. In order to improve the achievable secrecy rates, private jammers are employed to generate interference to confuse the eavesdroppers. These private jammers charge the legitimate transmitter for their jamming services based on the amount of interference received at the eavesdroppers. Therefore, this secrecy rate maximization problem is formulated as a Stackelberg game, in which the private jammers and the transmitter are the leaders and the follower of the game, respectively. A fixed interference price scenario is considered first, in which a closed-form solution is derived for the optimal amount of interference generated by the jammers to maximize the revenue of the legitimate transmitter. Based on this solution, the Stackelberg equilibrium of the proposed game, at which both legitimate transmitter and the private jammers achieve their maximum revenues, is then derived. Simulation results are also provided to validate these theoretical derivations.
This paper investigates secrecy rate optimization for a multicasting network, in which a transmitter broadcasts the same information to multiple legitimate users in the presence of multiple eavesdroppers. In order to improve the achievable secrecy rates, private jammers are employed to generate interference to confuse the eavesdroppers. These private jammers charge the legitimate transmitter for their jamming services based on the amount of interference received at the eavesdroppers. Therefore, this secrecy rate maximization problem is formulated as a Stackelberg game, in which the private jammers and the transmitter are the leaders and the follower of the game, respectively. A fixed interference price scenario is considered first, in which a closed-form solution is derived for the optimal amount of interference generated by the jammers to maximize the revenue of the legitimate transmitter. Based on this solution, the Stackelberg equilibrium of the proposed game, at which both legitimate transmitter and the private jammers achieve their maximum revenues, is then derived. Simulation results are also provided to validate these theoretical derivations.
This paper investigates secrecy rate optimization for a multicasting network, in which a transmitter broadcasts the same information to multiple legitimate users in the presence of multiple eavesdroppers. In order to improve the achievable secrecy rates, private jammers are employed to generate interference to confuse the eavesdroppers. These private jammers charge the legitimate transmitter for their jamming services based on the amount of interference received at the eavesdroppers. Therefore, this secrecy rate maximization problem is formulated as a Stackelberg game, in which the private jammers and the transmitter are the leaders and the follower of the game, respectively. A fixed interference price scenario is considered first, in which a closed-form solution is derived for the optimal amount of interference generated by the jammers to maximize the revenue of the legitimate transmitter. Based on this solution, the Stackelberg equilibrium of the proposed game, at which both legitimate transmitter and the private jammers achieve their maximum revenues, is then derived. Simulation results are also provided to validate these theoretical derivations.
Recently, physical layer security has been recognized as a new design paradigm to provide security in wireless networks. In contrast to the existing conventional cryptographic methods, physical layer security exploits the dynamics of fading channels to enhance security of wireless communications. This paper studies optimization frameworks for a multicasting network in which a transmitter broadcasts the same information to a group of legitimate users in the presence of multiple eavesdroppers. In particular, power minimization and secrecy rate maximization problems are investigated for a multicasting secrecy network. First, the power minimization problem is solved for different numbers of legitimate users and eavesdroppers. Next, the secrecy rate maximization problem is investigated with the help of private jammers to improve the achievable secrecy rates through a game theoretic approach. These jammers charge the transmitter for their jamming services based on the amount of interference caused to the eavesdroppers. For a fixed interference price scenario, a closed-form solution for the optimal interference requirement to maximize the revenue of the transmitter is derived. This rate maximization problem for a nonfixed interference price scenario is formulated as a Stackelberg game in which the jammers and transmitter are the leaders and follower, respectively. For the proposed game, a Stackelberg equilibrium is derived to maximize the revenues of both the transmitter and the private jammers. To support the derived theoretical results, simulation results are provided with different numbers of legitimate users and eavesdroppers. In addition, these results show that physical layer security based jamming schemes could be incorporated in emerging and future wireless networks to enhance the quality of secure communications.
In this paper, we study different secrecy rate optimization techniques for a multiple-input–multiple-output (MIMO) secrecy channel, where a multiantenna cooperative jammer is employed to improve secret communication in the presence of a multiantenna eavesdropper. Specifically, we consider two optimization problems, namely, power minimization and secrecy rate maximization. These problems are not jointly convex in terms of the transmit covariance matrices of the legitimate transmitter and the cooperative jammer. To circumvent these nonconvexity issues, we alternatively design the transmit covariance matrix of the legitimate transmitter and the cooperative jammer. For a given transmit covariance matrix at the cooperative jammer, we solve the power minimization and secrecy rate maximization problems based on a Taylor series expansion. Then, we propose two iterative algorithms to solve these approximated problems. In addition, we develop a robust scheme by incorporating channel uncertainties associated with the eavesdropper. By exploitingS-Procedure, we show that these robust optimization problems can be formulated into semidefinite programming. Moreover, we consider the secrecy rate maximization problem based on game theory, where the jammer introduces charges for its jamming service based on the amount of the interference caused to the eavesdropper. This secrecy rate maximization problem is formulated into aStackelberggame where the jammer and the transmitter are the leader and the follower of the game, respectively. For the proposed game,Stackelbergequilibrium is analytically derived. Simulation results have been provided to validate the convergence and performance of the proposed algorithms. In addition, it is shown that the proposed robust scheme outperforms the nonrobust scheme in terms of the achieved secrecy rate and the worst-case secrecy rate. Finally, theStackelbergequilibrium solution has been validated through numerical results.
In this paper, the problem of improving the secrecy communication for a cooperative jamming based network in which there is a friendly jammer, an eavesdropper and N pairs of sources and destinations is addressed. A Stackelberg game is formulated to tackle the joint utility maximization of the jammer and sources subject to a maximum jamming power constraint at the jammer. In this game, the jammer behaves as the leader and the sources are followers. A uniform pricing algorithm is proposed and we prove in theory that it can maximize the secrecy rate of the system. The Stackelberg equilibrium for the proposed game is characterized, and an effective distributed algorithm with guaranteed convergence is proposed. Numerical examples are presented to validate our analysis. It is shown that the proposed distributed algorithm can obtain almost the same performance as the optimal power allocation algorithm. Besides, we also investigate the performance of the proposed distributed algorithm in different scenarios. It is shown that the proposed algorithm can obtain significant improvement for secrecy rate in the sparse scenario where all the nodes are deployed sparsely. It can enhance the secrecy rate for about 0.3 dB.
This paper investigates the secrecy rate maximization problem for the multiple-input-single-output multiple-antenna-eavesdropper (MISOME) wiretap channel with multiple randomly located jammers. The multi-antenna base station (BS) transmits information signals along with artificial noise (AN) to disturb the eavesdropper. Moreover, the friendly jammers are properly selected to assist the legitimate link for better secure transmission with some payoffs. With this system model, we first formulate a Stackelberg game between the BS and the assisting jammers with full channel state information. Stackelberg equilibriums, including optimal fraction of transmit power for AN, optimal transmit power, and asking prices of assisting jammers, are first proved to exist and then analytically derived. A policy iterative algorithm is also proposed to obtain the optimal solutions. We then extend the Stackelberg game to the case of MISOME broadcast wiretap channel with channel distribution information of eavesdropper. Numerical results verify the accuracy of the derived results and the efficiency of the proposed algorithm. The results reveal that the proposed jammer-assisted secure transmission can greatly improve the secrecy performance and meanwhile save more energy for information signals, which is significant for future wireless communication.