Firefly Algorithm for Movable Antenna Arrays

Abstract

This letter addresses a multivariate optimization problem for linear movable antenna arrays (MAAs). Particularly, the position and beamforming vectors of the under-investigated MAA are optimized simultaneously to maximize the minimum beamforming gain across several intended directions, while ensuring interference levels at various unintended directions remain below specified thresholds. To this end, a swarm-intelligence-based firefly algorithm (FA) is introduced to acquire an effective solution to the optimization problem. Simulation results reveal the superior performance of the proposed FA approach compared to the state-of-the-art approach employing alternating optimization and successive convex approximation. This is attributed to the FA's effectiveness in handling non-convex multivariate and multimodal optimization problems without resorting approximations.

Figures (6)

• Figure 1: Radiation beam patterns. $N_A=8$. $L_0=\frac{\psi}{2}$; $L=8\psi$. $T=2$; $Q=2$; and $I_0=0.1$. Case 1: $\{ \theta_t\}^T_{t=1}=[100^{\circ}, 145^{\circ}]$ and $\{ \phi_q\}^Q_{q=1}=[125^{\circ}, 165^{\circ}]$. Case 2: $\{ \theta_t\}^T_{t=1}=[75^{\circ}, 150^{\circ}]$ and $\{ \phi_q\}^Q_{q=1}=[120^{\circ}, 170^{\circ}]$.
• Figure 2: Max-min beamforming gain versus the number of array antennas $N_A$ when $T=Q=2$.
• Figure 3: Max-min beamforming gain versus the number of undesired interference directions $Q$ when $T=2$, $N_A=8$.
• Figure 4: Max-min beamforming gain versus maximum number of population $\Omega$ with different numbers of $N_A$ and $I_0$ when $T=Q=2$.
• Figure 5: Max-min beamforming gain versus the maximum generations $R$ with different values of $\Omega$ and $I_0$ when $T=Q=2$ and $N_A=8$.