New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

Side lobe level reduction (SLL) of antenna arrays significantly enhances the signal -to -interference ratio and improves the quality of service (QOS) in recent and future wireless communication systems starting from 5G up to 7G. Furthermore, it improves the array gain and directivity, increasing the detection range and angular resolution of radar systems. This study proposes two highly efficient SLL reduction techniques. These techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm (GA) to develop the Conv/GA and DConv/GA, respectively. The convolution process determines the element's excitations while the GA optimizes the element spacing. For M elements linear antenna array (LAA), the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N = (2M-1). This new vector is divided into three different sets of excitations including the odd excitations, even excitations, and middle excitations of lengths M, M - 1, and M, respectively. When the same element spacing as the original LAA is used, it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with a much wider half -power beamwidth (HPBW). While the middle excitations give the same HPBW as the original LAA with a relatively higher SLL. To mitigate the increased HPBW of the odd and even excitations, the element spacing is optimized using the GA. Thereby, the synthesized arrays have the same HPBW as the original LAA with a two -fold reduction in the SLL. Furthermore, for extreme SLL reduction, the DConv/GA is introduced. In this technique, the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation vectors. It provides a relatively wider HPBW than the original LAA with about quad -fold reduction in the SLL.