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arXiv:1707.01351 · IEEE Wireless Communications Letters

Resource Allocations for Secure Cognitive Satellite-Terrestrial Networks

Bin Li, Zesong Fei, Xiaoming Xu, Zheng Chu

Published Sep 21, 2017• 88 citations
arXivHTMLPDF

Abstract

In this letter, we investigate the secure transmission in a cognitive satellite-terrestrial network with a multi-antennas eavesdropper, where the interference from terrestrial base station is introduced to enhance security of the satellite link. Our objective is to minimize the transmit power by jointly optimizing the cooperative beamforming and artificial noise while guaranteeing the secrecy rate constraint for satellite link, the information rate constraint for terrestrial link and the total transmit power constraint. Both scenarios of the perfect and imperfect channel cases are considered. The resulting optimization problems are non-convex and challenging, which are efficiently solved via certain transformations to be reformulated more tractable versions, respectively. Numerical results are provided to corroborate the proposed schemes.

DOI
10.1109/lwc.2017.2755014

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In this letter, we investigate the secure transmission in a cognitive satellite-terrestrial network with a multi-antennas eavesdropper, where the interference from terrestrial base station is introduced to enhance security of the satellite link. Our objective is to minimize the transmit power by jointly optimizing the cooperative beamforming and artificial noise while guaranteeing the secrecy rate constraint for satellite link, the information rate constraint for terrestrial link and the total transmit power constraint. Both scenarios of the perfect and imperfect channel cases are considered. The resulting optimization problems are non-convex and challenging, which are efficiently solved via certain transformations to be reformulated more tractable versions, respectively. Numerical results are provided to corroborate the proposed schemes.
This letter studies a secure multicast transmission design for cognitive satellite-terrestrial networks with multiple multi-antenna eavesdroppers, where the satellite (SAT) delivers a common confidential message to a group of legitimate users and the interference from the terrestrial base station (BS) is utilized to strengthen the security of the satellite link. Our goal is to minimize the total transmit power, subject to the secrecy rates for the satellite links as well as the information rate for the terrestrial link. The resulting optimization problem involves the joint optimization of the information covariance matrices at the SAT and the BS, which is non-convex and challenging. We have introduced a successive convex approximation method to transform the non-convex constraints into linear ones and proposed an effective iterative algorithm. Simulation results demonstrate the superiority of the proposed algorithm.
In this paper, we study the secure beamforming (BF) and artificial noise (AN) techniques for a cognitive satellite-terrestrial network, where a satellite system termed as the primary network under the interception of multiple unauthorized eavesdroppers shares the spectrum of resources with multi-cell secondary terrestrial networks. The design objective is to minimize the total transmit power on board the satellite and multiple terrestrial base stations (BSs) while meeting the secrecy rate constraint of the primary satellite user along with the signal-to-interference-plus-noise ratio constraints of secondary terrestrial users. When exact channel state information at the satellite and terrestrial BS is perfectly known, a zero-forcing scheme is first presented to obtain a sub-optimal solution with low complexity. Next, we propose a semi-definite relaxation-based iterative algorithm together with a golden section search method to achieve the optimal solution. Moreover, a joint BF and AN scheme is further explored to provide the additional degree-of-freedom against illegitimate eavesdropping. Eventually, numerical simulations are given to confirm the validity of the proposed schemes as well as the performance improvements compared with the existing approaches.
The research aims to be a secured multi-casts transmission for cognitive satellite-terrestrial networks with Multiple input and Multiple Output (MIMO) antenna eavesdroppers where the satellite provides the group of legitimate users with a common confidential message and where interferences from the terrestrial base station (BS) are used to enhance the safety of the satellite link. Idea of this work is to reduce total transmitting power, subject to satellite connectivity secrecy rates and the terrestrial link data rate. The resulting problem of optimization involves the joint optimization of the non-convex and challenging information covariance matrices at the SAT and the BS. In order to turn the non-convex constraints into linear ones, we introduced a successive concave approximation method and proposed an efficient iterative algorithm. The results of the simulation show that the proposed algorithm is superior.
This paper investigates the physical layer security of a satellite network, whose downlink spectral resource is shared with a terrestrial cellular network. We propose to employ a multi-antenna base station (BS) as a source of green interference to enhance secure transmission in the satellite network. By taking the mutual interference between these two networks into account, we first formulate a constrained optimization problem to maximize the instantaneous rate of the terrestrial user while satisfying the interference probability constraint of the satellite user. Then, with the assumption that imperfect channel state information (CSI) and statistical CSI of the link between the BS and satellite user are available at the BS, we present two beamforming (BF) schemes, namely, hybrid zero-forcing and partial zero-forcing to solve the optimization problem and obtain the BF weight vectors in a closed form. Moreover, we analyze the secrecy performance of primary satellite network by considering two practical scenarios, namely: Scenario I, the eavesdroppers CSI is unknown at the satellite and Scenario II, the eavesdroppers CSI is known at the satellite. Specifically, we derive the analytical expressions for the secrecy outage probability for Scenario I and the average secrecy rate for Scenario II. Finally, numerical results are provided to confirm the superiority of the proposed BF schemes and the validity of the performance analysis, as well as demonstrate the impacts of various parameters on the secrecy performance of the satellite network.
This article proposes a beamforming (BF) scheme for a cognitive satellite terrestrial network, where the base station (BS) and a cooperative terminal (CT) are exploited as green interference resources to enhance the system security performance in the presence of unknown eavesdroppers. Different from the related works, we assume that only imperfect channel information of the mobile user (MU) and earth station (ES) is available. Specifically, we formulate an optimization problem with the objective to degrade the possible wiretap channels within the private signal beampattern region, while imposing constraints on the signal-to-interference-plus-noise ratio (SINR) at the MU, the interference level of the ES and the total transmit power budget of the BS and CT. To solve this mathematically intractable problem, we propose a joint artificial noise generation and cooperative jamming BF scheme to suppress the interception. Finally, the effectiveness and superiority of the proposed BF scheme are confirmed through computer simulations.
This paper proposes a beamforming (BF) scheme based on multi-objective optimization (MOO) for a cognitive satellite-terrestrial network (CSTN), where the eavesdroppers (Eves) attempt to intercept the private message conveyed to an intended user. By considering that the maximization of achievable secrecy rate (ASR) and the minimization of transmit power can not be achieved simultanesouly, we adopt artificial noise (AN) and apply the weighted Tchebycheff approach to formulate a multi-object optimization problem. Then, by introducing several auxiliary variables, applying semidefinite programming (SDP) and means of Taylor approximation, the original nonconvex MOO problem is transformed into a convex one with a series of linear constraints, and the BF weight vector and AN vector can be obtained. Finally, simulation results are given to illustrate the tradeoff between the conflicting objectives and the superiority of the proposed algorithm in comparison with the commonly used schemes.