Directional Modulation via Symbol-Level Precoding: A Way to Enhance Security

Wireless communication provides a wide coverage at the cost of exposing information to unintended users. As an information-theoretic paradigm, secrecy rate derives bounds for secure transmission when the channel to the eavesdropper is known. However, such bounds are shown to be restrictive in practice and may require exploitation of specialized coding schemes. In this paper, we employ the concept of directional modulation and follow a signal processing approach to enhance the security of multi-user MIMO communication systems when a multi-antenna eavesdropper is present. Enhancing the security is accomplished by increasing the symbol error rate at the eavesdropper. Unlike the information-theoretic secrecy rate paradigm, we assume that the legitimate transmitter is not aware of its channel to the eavesdropper, which is a more realistic assumption. We examine the applicability of MIMO receiving algorithms at the eavesdropper. Using the channel knowledge and the intended symbols for the users, we design security enhancing symbol-level precoders for different transmitter and eavesdropper antenna configurations. We transform each design problem to a linearly constrained quadratic program and propose two solutions, namely the iterative algorithm and one based on non-negative least squares, at each scenario for a computationally-efficient modulation. Simulation results verify the analysis and show that the designed precoders outperform the benchmark scheme in terms of both power efficiency and security enhancement.Comment: This manuscript is submitted to IEEE Journal of Selected Topics in Signal Processin

Robust Adaptive Beamforming for General-Rank Signal Model with Positive Semi-Definite Constraint via POTDC

The robust adaptive beamforming (RAB) problem for general-rank signal model with an additional positive semi-definite constraint is considered. Using the principle of the worst-case performance optimization, such RAB problem leads to a difference-of-convex functions (DC) optimization problem. The existing approaches for solving the resulted non-convex DC problem are based on approximations and find only suboptimal solutions. Here we solve the non-convex DC problem rigorously and give arguments suggesting that the solution is globally optimal. Particularly, we rewrite the problem as the minimization of a one-dimensional optimal value function whose corresponding optimization problem is non-convex. Then, the optimal value function is replaced with another equivalent one, for which the corresponding optimization problem is convex. The new one-dimensional optimal value function is minimized iteratively via polynomial time DC (POTDC) algorithm.We show that our solution satisfies the Karush-Kuhn-Tucker (KKT) optimality conditions and there is a strong evidence that such solution is also globally optimal. Towards this conclusion, we conjecture that the new optimal value function is a convex function. The new RAB method shows superior performance compared to the other state-of-the-art general-rank RAB methods.Comment: 29 pages, 7 figures, 2 tables, Submitted to IEEE Trans. Signal Processing on August 201

Development and implementation of an adaptive digital beamforming network for satellite communication systems

The use of adaptive digital beamforming techniques has, until recently, been largely restricted to high performance military radar systems. Recent advances in digital technology, however, have enabled the design of single chip digital beamforming networks. This, coupled with advances in digital signal processor technology, enables complete beamforming systems to be constructed at a lower cost, thus making the application of these techniques to commercial communications systems attractive. The design and development of such an adaptative digital beamforming network are described. The system is being developed as a proof of concept laboratory based demonstrator to enable the feasibility of adaptive digital beamforming techniques for communication systems to be determined. Ultimately, digital beamforming could be used in conjunction with large array antennas for communication satellite systems. This will enable the simultaneous steering of high gain antenna beams in the direction of gr...Peer ReviewedPostprint (published version

Quadratically Constrained Beamforming Robust Against Direction-of-Arrival Mismatch

It is well known that the performance of the minimum variance distortionless response (MVDR) beamformer is very sensitive to steering vector mismatch. Such mismatches can occur as a result of direction-of-arrival (DOA) errors, local scattering, near-far spatial signature mismatch, waveform distortion, source spreading, imperfectly calibrated arrays and distorted antenna shape. In this paper, an adaptive beamformer that is robust against the DOA mismatch is proposed. This method imposes two quadratic constraints such that the magnitude responses of two steering vectors exceed unity. Then, a diagonal loading method is used to force the magnitude responses at the arrival angles between these two steering vectors to exceed unity. Therefore, this method can always force the gains at a desired range of angles to exceed a constant level while suppressing the interferences and noise. A closed-form solution to the proposed minimization problem is introduced, and the diagonal loading factor can be computed systematically by a proposed algorithm. Numerical examples show that this method has excellent signal-to-interference-plus-noise ratio performance and a complexity comparable to the standard MVDR beamformer