Spatially Selective Artificial-Noise Aided Transmit Optimization for MISO Multi-Eves Secrecy Rate Maximization (1303.1915v1)

Abstract: 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.

• The paper introduces a joint optimization of confidential information and artificial noise to maximize secrecy rate in MISO channels against multiple eavesdroppers.
• It reformulates secrecy rate maximization into a single-variable problem solved via a sequence of semidefinite programs, enhancing the system's performance.
• Results demonstrate that optimized transmit beamforming with adaptive AN yields significantly superior secrecy rates compared to traditional no-AN or isotropic AN methods.

Overview of Artificial-Noise Aided Transmit Optimization for MISO Multi-Eves Secrecy Rate Maximization

The paper "Spatially Selective Artificial-Noise Aided Transmit Optimization for MISO Multi-Eves Secrecy Rate Maximization," authored by Qiang Li and Wing-Kin Ma, explores the nuanced challenge of enhancing physical-layer security in MISO (Multi-Input Single-Output) channels subject to eavesdropping. Specifically, the authors aim to optimize combined confidential information and artificial noise (AN) covariance to maximize secrecy rate under power constraints.

Key Concepts and Methodologies

The paper considers a scenario with multiple eavesdroppers (Eves) and leverages the potential of AN to strategically interfere with them, thereby safeguarding confidentiality. Unlike past approaches that often pre-fixed AN as isotropic or did not jointly optimize it with confidential transmission, this research proposes a more flexible design by allowing AN to have any spatial pattern.

The method transforms the secrecy rate maximization (SRM) problem into a single-variable optimization problem, rendering it solvable via a sequence of semidefinite programs (SDPs). This transformation is a pivotal contribution, as it helps derive a solution without the limitations imposed by previously considered structural constraints on AN.

Numerical Results and Contributions

Simulations highlight the proposed design's significant performance advantages, especially when there are many eavesdroppers. This indicates that deploying AN not only as interference but carefully optimized can lead to superior secrecy rates compared to both optimal no-AN and isotropic AN approaches. Furthermore, the established framework generalizes to a robust formulation for scenarios with imperfect channel state information (CSI), addressing real-world constraints such as interference temperature in cognitive radio networks.

The authors also prove that transmit beamforming emerges as an optimal SRM strategy, reinforcing its applicability in both perfect and imperfect CSI scenarios. This finding is theoretically enriching as it offers a robust justification for its adoption in secure communications design.

Implications and Future Directions

The paper's implications span the adoption of AN schemes in wireless communications to ensure confidentiality under practical constraints. It opens up pathways for optimized secure communication techniques applicable in various multi-antenna scenarios, including cognitive radio and multicell interference environments.

Future research could explore more differentiated AN patterns tailored to varying eavesdropping threat levels or refine robust designs to encompass fluctuating environmental parameters and constraints. Another potential direction lies in analyzing conditions under which isotropic AN might approach optimality, providing further insights into simplifying secure transmission designs without compromising on performance.

In conclusion, this paper provides a comprehensive mathematical toolset and strategic insights into optimizing secure MISO transmission, fostering advancements in secure wireless communication technology.