A novel linear and planar multiband fractal‐shaped antenna arrays with low sidelobes

Summary Fractal antenna arrays are usually used to tune multiband frequencies. However, these types of iteratively constructed antennas are associated with undesirable high sidelobe levels and low directivities. In this paper, an optimization procedure based on the genetic algorithm is used to find the optimal weights of the array elements such that the corresponding array patterns have low sidelobe levels and good directivity. Moreover, the fractal nature in the proposed arrays is maintained regardless of the optimized weights. Thus, the proposed fractal‐shaped array maintains its capability in performing multiband frequency operation. These good radiation features make the proposed fractal‐shaped array more appropriate for the current and future wireless communication applications. Simulation results confirm the superiority of the presented linear and planar fractal‐shaped array structures with compared to the conventional fractal cantor linear array and the standard Sierpinski carpet planar array. For the proposed fractal cantor linear array, the sidelobe level has been reduced to more than −20 dB at different operating frequencies, and the directivity has been improved by more than 8 dB, while the modified Sierpinski carpet planar array has achieved −30 dB depressions in the sidelobe level and 6 dB improvement in the directivity. Fractal antenna arrays are most promising ones for multiband applications. However, these types of iteratively constructed antennas are associated with undesirable high sidelobe levels and low directivities. In this paper, a genetic optimization algorithm based on the desired constraint masks is used to optimize the weights of the fractal array elements such that its corresponding radiation pattern has low sidelobe level and good directivity. More important, the fractal nature and its multiband operation capability are maintained. Furthermore, the problem of the growing complexity in the fractal arrays has been efficiently solved by introducing fractal‐clustered shapes instead of its elemental counterpart.