Atom search optimization algorithm based hybrid antenna array receive beamforming to control sidelobe level and steering the null

Abstract Modern and upcoming 5G cellular communication networks require large-scale antenna systems to improve gain in order to face the weaknesses in the suggested millimeter-wave (mmWave) signals and to meet the higher capacity demand. These systems implement digital beamforming which requires radio frequency (RF) chain connected to each antenna. Hybrid analog-digital (HAD) beamforming has been proposed as a solution to many of the challenges facing the implementation of fully-digital beamforming, namely, energy consumption, hardware cost, and complexity. The main goals of this paper are to reduce the peak sidelobe level (SLL) of the beam pattern and steering the nulls in the desired directions along with reducing the power consumption, hardware cost, and complexity. To achieve these goals, a novel partial-connected hybrid analog-digital receive beamformer based on an atom search optimization (ASO) algorithm is proposed. ASO is a recently invented metaheuristic optimization algorithm imitating the physical movements of atoms as described in molecular dynamics simulation with the advantage of having few parameters to tune. The desired optimization weights are accomplished by controlling the amplitudes-only of the digital beamformer’s vector along with the phase shifts of the analog beamformer’s vectors. Finally, to demonstrate the effectiveness of the proposed beamformer, a comparison with other state-of-the-art metaheuristic algorithms through several scenarios is conducted, in terms of peak SLL reduction, null depth, and convergence rates. Simulation results show that the proposed algorithm has always been outperforming other types of beamformers.