An Efficient Hybrid Beamforming Design for Massive MIMO Receive Systems via SINR Maximization Based on an Improved Bat Algorithm

Mohammed A. Almagboul, Feng Shu, Yaolu Qin, Xiaobo Zhou, Jin Wang, Yuwen Qian, Kingsley Jun Zou

Hybrid analog and digital (HAD) beamforming has been recently receiving considerable deserved attention for a practical implementation on the large-scale antenna systems. As compared to full digital beamforming, partial-connected HAD beamforming can significantly reduce the hardware cost, complexity, and power consumption. In this paper, in order to mitigate the jamming along with lowering the hardware complexity and cost by reducing the number of RF chains needed, a novel hybrid analog and digital receive beamformer based on an improved bat algorithm (I-BA) and the phase-only is proposed. Our proposed beamformer is compared with robust adaptive beamformers (RABs) methods proposed by us, which are considered in the digital beamforming part. The evolutionary optimization algorithm is proposed since most of the RAB methods are sensitive to the DOA mismatch, and depending on the complex weights, resulting in an expensive receiver. In the analog part, analog phase alignment by linear searching (APALS) with a sufficiently fine grid of points is employed to optimize the analog beamformer matrix. The performance of the proposed I-BA is revealed using MATLAB simulation and compared with BA, and Particle swarm optimization (PSO) algorithms, which shows a better performance in terms of convergence speed, stability, and the ability to jump from the local minima.

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