Beamforming of Transmit Antennas Using Grey Wolf Optimization and L2-Norm for Performance Enhancement of Beyond 5G Communications

Abstract

Pattern synthesis is widely used in many radar and communication systems and received great interest. So, this paper proposes a new beamforming strategy based on a hybrid combination between grey wolf optimizer (GWO) with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm { L}}_{2}$ </tex-math></inline-formula>-norm called proposed GWO. This approach is applied to synthesized uniform linear arrays (ULA), Chebyshav arrays, and shaped pattern arrays. Moreover, it is utilized for side lobe level (SLL) and size reduction of antenna elements. In this strategy, the GWO is utilized to optimize the element spacing to adjust the half-power beamwidth (HPBW) to save it the same as desired pattern. Furthermore, the excitations of the antenna elements are optimized via the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm { L}}_{2}$ </tex-math></inline-formula>-norm minimization problem. The proposed GWO has low complexity (fewer iterations and computing time) compared to other algorithms. In addition, it has a very accurate approximation of the original radiation pattern. As well, the computer simulation technology (CST) microwave package is utilized to achieve the practical validation of the proposed methodologies. As an application of the proposed GWO, it is employed to create a proposed hybrid beamforming (PHB) structure for Multi-input Multi-output (MIMO) systems. Consequently, the BS transmitting antennas are synthesized for gain maximization while utilizing the current amount of antenna elements. This results in considerable savings in antenna components and associated radio frequency (RF) chains which reduces system complexity. Furthermore, array gain maximization will increase the received signal-to-noise ratio (SNR). In addition, the SLL reduction scenario will decrease the interference from undesired users which in turn will also increase SNR. Hence, the performance of the system in terms of spectral efficiency (SE) and power utilization will be improved.