Peak Sidelobe Suppression in Planar Fluid Antenna Array

2026-06-30

AI Summary

This study addresses the challenge of suppressing peak sidelobe level (PSLL) in sparse planar fluid antenna arrays under stringent sparsity constraints. To this end, an improved genetic algorithm (IGA) is proposed that optimizes the port activation pattern to significantly reduce PSLL while preserving the mainlobe width. The IGA integrates tournament selection, adaptive operator probabilities, hybrid crossover, multi-point mutation, and an elitist pool retention strategy to enhance both convergence speed and solution quality. Experimental results demonstrate that, compared to conventional genetic algorithms, the proposed method achieves a 4.45 dB reduction in PSLL with nearly identical mainlobe width, thereby confirming its superior performance in pattern synthesis for sparse arrays.

Abstract

Fluid antenna systems (FAS) have emerged as a promising technology for next-generation wireless communications, offering inherent reconfigurability and spatial adaptability. A distinctive and practically consequential property of fluid antenna arrays (FAAs) is their geometric diversity: by dynamically activating different subsets of spatially distributed ports across a dense discrete grid, a FAA can reconfigure its effective aperture geometry on demand, thereby unlocking unprecedented spatial degrees of freedom for radiation pattern synthesis. Exploiting such geometric flexibility, this paper investigates peak sidelobe level (PSLL) minimization in sparse planar FAAs through enhanced heuristic optimization. Specifically, an improved genetic algorithm (IGA) is proposed to determine the optimal port activation pattern that minimizes the PSLL under strict sparsity constraints. The proposed IGA incorporates tournament selection, adaptive operator probabilities, a hybrid crossover scheme, multi-point mutation, and an elite-pool preservation strategy to improve both convergence speed and solution quality. Simulation results demonstrate that the IGA significantly outperforms the canonical GA (CGA) in convergence behavior and final PSLL performance, achieving a 4.45 dB reduction in sidelobe levels while maintaining a comparable mainlobe width.

Problem

• Peak Sidelobe Level
• Fluid Antenna Array
• Planar Array
• Sparsity Constraint
• Radiation Pattern Synthesis

Innovation

• fluid antenna array
• peak sidelobe level suppression
• improved genetic algorithm
• geometric diversity
• sparse array optimization