Spatially Selective Artificial-Noise Aided Transmit Optimization for MISO Multi-Eves Secrecy Rate Maximization

Consider an MISO channel overheard by multiple eavesdroppers. Our goal is to design an artificial noise (AN)-aided transmit strategy, such that the achievable secrecy rate is maximized subject to the sum power constraint. AN-aided secure transmission has recently been found to be a promising approach for blocking eavesdropping attempts. In many existing studies, the confidential information transmit covariance and the AN covariance are not simultaneously optimized. In particular, for design convenience, it is common to prefix the AN covariance as a specific kind of spatially isotropic covariance. This paper considers joint optimization of the transmit and AN covariances for secrecy rate maximization (SRM), with a design flexibility that the AN can take any spatial pattern. Hence, the proposed design has potential in jamming the eavesdroppers more effectively, based upon the channel state information (CSI). We derive an optimization approach to the SRM problem through both analysis and convex conic optimization machinery. We show that the SRM problem can be recast as a single-variable optimization problem, and that resultant problem can be efficiently handled by solving a sequence of semidefinite programs. Our framework deals with a general setup of multiple multi-antenna eavesdroppers, and can cater for additional constraints arising from specific application scenarios, such as interference temperature constraints in interference networks. We also generalize the framework to an imperfect CSI case where a worst-case robust SRM formulation is considered. A suboptimal but safe solution to the outage-constrained robust SRM design is also investigated. Simulation results show that the proposed AN-aided SRM design yields significant secrecy rate gains over an optimal no-AN design and the isotropic AN design, especially when there are more eavesdroppers.Comment: To appear in IEEE Trans. Signal Process., 201

Robust Beamforming for Two-Way Relay Systems

In wireless communication systems, relays are widely used to extend coverage. Over the past years, relays have evolved from simple repeaters to more sophisticated units that perform signal processing to improve signal to interference plus noise ratio (SINR) or throughput (or both) at the destination receiver. There are various types of relays such as amplify and forward (AF), decode and forward (DF), and compress and forward (CF) (or estimate and forward (EF)) relays. In addition, recently there has been a growing interest in two-way relays (TWR). By utilizing the concept of analog network coding (ANC), TWRs can improve the throughput of a wireless sys- tem by reducing the number of time slots needed to complete a bi-directional message exchange between two destination nodes. It’s well known that the performance of a TWR system greatly depends on its ability to apply signal processing techniques to effectively mitigate the self-interference and noise accumulation, thereby improving the SINR. We study a TWR system that is equipped with multiple antennas at the relay node and a single antenna at the two destination nodes. Different from traditional work on TWR, we focus on the case with imperfect knowledge of channel state information (CSI). For such a TWR, we formulate a robust optimization problem that takes into ac- count norm-bounded estimation errors in CSI and designs an optimal beamforming matrix. Realizing the fact that this problem is extremely hard to solve globally, we derive two different methods to obtain either optimal or efficient suboptimal beam- forming matrix solutions. The first method involves solving the robust optimization problem using the S-procedure and semidefinite programming (SDP) with rank-one relaxation. This method provides an optimal solution when the rank-one relaxation condition for the SDP is satisfied. In cases where the rank-one condition cannot be satisfied, it’s necessary to resort to sub-optimal techniques. The second approach presented here reformulates the robust non-convex quadratically constrained quadratic programming (QCQP) into a robust linear programming (LP) problem by using first-order perturbation of the optimal non-robust beamforming solution (which assumes no channel estimation error). Finally, we view the TWR robust beamforming problem from a practical standpoint and develop a set of iterative algorithms based on Newton’s method or the steepest descent method that are practical for hardware implementation