Multiple-input Multiple-output Multiple-eavesdropper Research Articles

A Rotation-Based Method for Precoding in Gaussian MIMOME Channels

The problem of maximizing secrecy rate of multiple-input multiple-output multiple-eavesdropper (MIMOME) channels with arbitrary numbers of antennas at each node is studied in this paper. First, the optimization problem corresponding to the secrecy capacity of the MIMOME channel is converted to an equivalent optimization based on Givens rotations and eigenvalue decomposition of the covariance matrix. In this new formulation, precoder is a rotation matrix which results in a positive semi-definite (PSD) covariance matrix by construction. This removes the PSD matrix constraint and makes the problem easier to tackle. Next, a Broyden-Fletcher-Goldfarb-Shanno (BFGS)-based algorithm is developed to find the rotation and power allocation parameters. Further, the generalized singular value decomposition (GSVD)-based precoding is used to initialize this algorithm. The proposed rotation-BFGS method provides an efficient approach to find a near-optimal transmit strategy for the MIMOME channel and outperforms various state-of-the-art analytical and numerical methods. In particular, the rotation-BFGS precoding achieves higher secrecy rates than the celebrated GSVD precoding, with a reasonably higher computational complexity. Extensive numerical results elaborate on the effectiveness of the rotation-BFGS precoding. The new framework developed in this paper can be applied to a variety of similar problems in the context of multi-antenna channels with and without secrecy.

Realizing Efficient Security and Privacy in IoT Networks.

In recent times, security and privacy at the physical (PHY) layer has been a major issue of several communication technologies which comprise the internet of things (IoT) and mostly, the emerging fifth-generation (5G) cellular network. The most real-world PHY security challenge stems from the fact that the passive eavesdropper’s information is unavailable to the genuine source and destination (transmitter/receiver) nodes in the network. Without this information, it is difficult to optimize the broadcasting parameters. Therefore, in this research, we propose an efficient sequential convex estimation optimization (SCEO) algorithm to mitigate this challenge and improve the security of physical layer (PHY) in a three-node wireless communication network. The results of our experiments indicate that by using the SCEO algorithm, an optimal performance and enhanced convergence is achieved in the transmission. However, considering possible security challenges envisaged when a multiple eavesdropper is active in a network, we expanded our research to develop a swift privacy rate optimization algorithm for a multiple-input, multiple-output, multiple-eavesdropper (MIMOME) scenario as it is applicable to security in IoT and 5G technologies. The result of the investigation show that the algorithm executes significantly with minimal complexity when compared with nonoptimal parameters. We further employed the use of rate constraint together with self-interference of the full-duplex transmission at the receiving node, which makes the performance of our technique outstanding when compared with previous studies.

Precoding and Artificial Noise Design for Cognitive MIMOME Wiretap Channels

In this paper, we study the secrecy precoding problem for a cognitive multiple-input–multiple-output multiple-eavesdropper (MIMOME) wiretap system. The problem is studied with an artificial noise (AN)-aided precoding scheme. First, we place AN in the null space of the legitimate receiver's channel matrix and formulate a secrecy rate maximization (SRM) problem, which is subject to both an interference power constraint imposed to protect the primary user (PU) and a maximum transmit power constraint available for the secondary transmitter. Second, we drop the null space constraint and reformulate the SRM problem. Because the formulated SRM problems naturally constitute difference-convex-type programming problems, we solve them by employing a successive convex approximation method, where the nonconvex parts of each problem are approximated by their first-order Taylor expansion. Thus, the SRM problems can be iteratively solved through successive convex programming of their convexified versions. Results show that our algorithms can achieve a satisfactory solution with guaranteed convergence.