Inspired by the process of migration and reproduction of flora, this paper proposes a novel artificial flora (AF) algorithm. This algorithm can be used to solve some complex, non-linear, discrete optimization problems. Although a plant cannot move, it can spread seeds within a certain range to let offspring to find the most suitable environment. The stochastic process is easy to copy, and the spreading space is vast; therefore, it is suitable for applying in intelligent optimization algorithm. First, the algorithm randomly generates the original plant, including its position and the propagation distance. Then, the position and the propagation distance of the original plant as parameters are substituted in the propagation function to generate offspring plants. Finally, the optimal offspring is selected as a new original plant through the selection function. The previous original plant becomes the former plant. The iteration continues until we find out optimal solution. In this paper, six classical evaluation functions are used as the benchmark functions. The simulation results show that proposed algorithm has high accuracy and stability compared with the classical particle swarm optimization and artificial bee colony algorithm.

Dual-function radar-communication (DFRC) systems is a promising technology that has been evolved as a solution for frequency spectrum congestion in recent years. In this paper, we propose DFRC strategy by employing the concept of sidelobe manipulation. Specifically, we exploit the beam orthogonality associated with N × N Butler matrix (BM) connected with N element frequency diverse array (FDA). The proposed scheme utilizes BM parallel excited by radar signals while communication signal is injected into the system alongside the radar signal. In doing so, the communication signal is projected along the null radiation direction of the radar main beam concurrently. Furthermore, due to the beam orthogonality feature of the BM, there is no cross-interference between the communication signal and radar target detection. Numerical examples are provided for performance evaluations and to demonstrate the efiectiveness of proposed system.

In this paper, we study how to improve the communication quality in millimeter-wave (mmWave) ground-to-unmanned aerial vehicle (UAV) communication systems. The system consists of a ground node with an antenna array and a UAV with a single antenna. Obviously, the communication quality can be maximally enhanced in the case that the trajectories of array beampattern peak and UAV coincide with each other. Achieving this objective through a traditional phased array (PA) approach requires considerable implementation costs, as the phase lags among array elements need to be frequently updated to change the beampattern peak direction. To alleviate this, we design an efficient method based on frequency diverse array (FDA), which tunes the frequency offsets among array elements. As a result, FDA generates a time-variant beampattern, such that the beampattern peak exhibits autonomous spatial movement. By using this characteristic, the complexity of the FDA method is reduced compared to PA, since we can design the beampattern peak trajectory without the necessity for real-time parameter updates. Specifically, subject to the constraints of UAV's speed, flight time and FDA's frequency offsets range, we jointly optimize the UAV movement and FDA frequency offsets to maximally align the trajectories of UAV and FDA beampattern peak, thereby improving beampattern gain. However, the formulated problem is non-convex with highly coupled variables. To handle this problem, we further design an algorithm based on penalty dual decomposition (PDD) method which circularly updates the variables, and finally yields a stationary solution of the problem. Simulation results demonstrate that, with the same implementation complexity, the FDA approach can achieve a higher communication quality compared to the PA approach and other benchmark schemes.

Physical-layer (PHY) security is widely used as an effective method for ensuring secure wireless communications. However, when the legitimate user (LU) and the eavesdropper (Eve) are in close proximity, the channel coupling can significantly degrade the secure performance of PHY. Frequency diverse array (FDA) technique addresses channel coupling issues by introducing frequency offsets among array elements. However, FDA’s ability to secure communication relies mainly on frequency domain characteristics, lacking the spatial degrees of freedom. The recently proposed movable antenna (MA) technology serves as an effective approach to overcome this limitation. It offers the flexibility to adjust antenna positions dynamically, thereby further decoupling the channels between LU and Eve. In this paper, we propose a novel MA-FDA approach, which offers a comprehensive solution for enhancing PHY security. We aim to maximize the achievable secrecy rate through the joint optimization of all antenna positions at the base station (BS), FDA frequency offsets, and beamformer, subject to the predefined regions for antenna positions, frequency offsets range, and energy constraints. To solve this non-convex optimization problem, which involves highly coupled variables, the alternating optimization (AO) method is employed to cyclically update the parameters, with the projected gradient ascent (PGA) method and block successive upper-bound minimization (BSUM) method being employed to tackle the challenging subproblems. Simulation results demonstrate that the MA-FDA approach can achieve a higher secrecy rate compared to the conventional phased array (PA) or fixed-position antenna (FPA) schemes.