Robust Beamforming Design for Secure Multiuser MISO Interference Channel

Xu, Hongbo; Zhu, Bingru; Liu, Jun; Zhou, Anyu

doi:10.1109/lcomm.2016.2643159

Cited by 12 publications

(13 citation statements)

References 13 publications

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“…We adopt the on–off transmission scheme [15] in this paper, Alice chooses a constant secrecy rate in transmitter. Moreover, Alice designs achievable secrecy rate

R_{S_{k}}

for user k , this method is widely used in previous research papers like [24, 33].…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Secure beamforming method for artificial‐noise‐aided multiuser broadcast system with users of different importance under secrecy outage probability constraint

et al. 2020

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“…We adopt the on–off transmission scheme [15] in this paper, Alice chooses a constant secrecy rate in transmitter. Moreover, Alice designs achievable secrecy rate

R_{S_{k}}

for user k , this method is widely used in previous research papers like [24, 33].…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…The authors of [23] designed the optimal broadcast transmit BF matrix by a simple structure with intuitive properties and interpretations. And authors of [24] designed a robust BF method for secure multiuser MISO interference channel.…”

Section: Introductionmentioning

confidence: 99%

Secure beamforming method for artificial‐noise‐aided multiuser broadcast system with users of different importance under secrecy outage probability constraint

et al. 2020

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“…A second approach leads to a robust solution for the problem by considering a closed form expression for the CSI uncertainty. In particular, the authors of [11, 23–25] consider a robust solution when imperfect instantaneous‐based CSI is used while the authors of [26–29] consider the solution for the case of covariance‐based CSI. This second approach outperforms the approach in [21, 22].…”

Section: Introductionmentioning

confidence: 99%

“…The main contribution of our work is that we obtain a robust solution for the beamforming vector by following the SCA method, where we derive the beamforming vectors directly and avoid using the SDR method, following the previous work [11, 21–30], since the SDR method gives the solution by either ignoring the rank constraint (which requires the additional approximation step of reformulation) or by using a general rank beamforming technique, which reduces the rate of the system. We find the worst case CSI by modifying theorem 1 in [31], where Mohannad and Ho [31] found a solution of

bold-italicX

in the problem (

MaxTr false(X Y false)

) when

bold-italicY

is known and positive (semi)definite.…”

Section: Introductionmentioning

confidence: 99%

Worst case fair beamforming for multiple multicast groups in multicell networks

et al. 2019

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“…A second approach leads to a robust solution for the problem by considering a closed form expression for the CSI uncertainty. In particular, the authors in [76,[88][89][90] consider a robust solution when imperfect instantaneous based CSI is used while the authors in [91][92][93][94] consider the solution for the case of covariance based CSI. This second approach outperforms the approach in [86,87].…”

Section: Introductionmentioning

confidence: 99%

Sub-optimal and robust downlink beamforming in wireless networks with imperfect channel state information

Al-Asadi¹

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The rapid growth in internet applications such as video streaming enforce the researcher to explore a new wireless technique to ensure high signal to interference noise ratio (SINR) at the end users, leading to high quality of service (QOS). The fourth generation (4G) wireless technologies introduced a with promising technique known as multiple-input-multiple-output (MIMO) paradigm. The MIMO offers spatial diversity of multiple signals between the source and the destination which can ensure high concentration of the desired power at the destination as well as combat the unwanted interference which can be done by the beamforming technique, implemented in two ways the up-link and the down-link. Two methods of beamforming have been addressed in MIMO wireless communications, the first consider the minimization of transmitted power for predefined SINR at the receiver and the second approach consider maximization of SINR at the recipient while maintaining the power at the sender to a small fixed value. Rigid beamforming is assured when the accurate channel state information (CSI) of the wireless system are acquired at the beamforming side. Because of some practical limitations in wireless systems such as feedback error, dynamic characteristics of wireless channel, etc., the ideal CSI cannot be obtained and thus the beamforming must consider the error in CSI. Three type of solutions have been developed to combat the effect of uncertain CSI these solutions are the non-robust, the sub-optimal and the robust solution. In this work the sub-optimal and the robust downlink beamforming in conventional wireless network are addressed. The solution considers a multicast, multi-group, multicell scenario. The uncertainty in CSI is modeled mathematically using Frobinius norm and the beamforming method used is the QOS method where the minimum SINR over all groups is maximized for small predefined transmitted power. Because the problem is difficult to be solved as a single optimization problem, it is divided into two problems. The first problem eliminates the effect of CSI uncertainty using the non-monotone spectral projected gradient (NMSPG) method, and the second problems use the successive convex approximation (SCA) method to extract the beamforming vectors for each group. The procedure goes through an iterate-alternative convex technique between the two methods until stopped by some predefined criteria. Wireless communication researchers have also achieved significant development in the area of spectrum scarcity by introducing the cognitive radio (CR) network. In a CR network the secondary users (SUs) can utilize the licensed frequency that is underutilized by the primary users (PUs). Two type of CR network were developed, the overlay and the underlay CR network. The beamforming in an overlay CR network follows the same procedure as in a conventional network while in an underlay CR network an extra constraint must be added to the beamforming problem which makes the problem more difficult to solved. In this thesis the beamforming problem in a CR network with multiple secondary transmitters that generate multiple beamforming vectors to multiple groups of secondary receivers under uncertain CSI are analyzed and solved. Two solutions were developed: the sub-optimal and the robust solutions. For the sub-optimal solution, the problem is split into two problems, the QOS and the interference power problem to combat the effect of CSI uncertainty then the two problems are combined to find the beamforming vectors using the SCA method. For the robust solution, the problem is also divided into two problems, the QOS and the interference power problem to eliminate the CSI uncertainty. The interference power problem is solved using the Lagrangian duality. The QOS problem is solved using the Lagrangian duality and the NMSPG method. after addressing CSI uncertainty, the beamforming vectors are extracted using the SCA method and, solved using the bisection search method.

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Robust Beamforming Design for Secure Multiuser MISO Interference Channel

Cited by 12 publications

References 13 publications

Secure beamforming method for artificial‐noise‐aided multiuser broadcast system with users of different importance under secrecy outage probability constraint

Secure beamforming method for artificial‐noise‐aided multiuser broadcast system with users of different importance under secrecy outage probability constraint

Worst case fair beamforming for multiple multicast groups in multicell networks

Sub-optimal and robust downlink beamforming in wireless networks with imperfect channel state information

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